From 7bdd01237ca07860ee10022147d950baef88e43f Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 9 Sep 2020 20:37:46 -0400 Subject: [PATCH 01/18] add microarray gene id conversion notebook - make appropriate updates to `references.bib`, `Snakefile`, and `_navbar.html` file --- 01-getting-started/getting-started.html | 14 +- 02-microarray/00-intro-to-microarray.html | 14 +- .../clustering_microarray_01_heatmap.html | 67 +- ...dimension-reduction_microarray_01_pca.html | 71 +- ...imension-reduction_microarray_02_umap.html | 79 +- .../gene-id-convert_microarray_01_ensembl.Rmd | 272 +++ ...gene-id-convert_microarray_01_ensembl.html | 2046 +++++++++++++++++ 03-rnaseq/00-intro-to-rnaseq.html | 14 +- 03-rnaseq/clustering_rnaseq_01_heatmap.html | 120 +- .../differential-expression_rnaseq_01.html | 186 +- .../dimension-reduction_rnaseq_01_pca.html | 100 +- .../dimension-reduction_rnaseq_02_umap.html | 107 +- .../00-intro-to-advanced-topics.html | 14 +- Snakefile | 1 + components/_navbar.html | 1 + references.bib | 6 + 16 files changed, 2693 insertions(+), 419 deletions(-) create mode 100644 02-microarray/gene-id-convert_microarray_01_ensembl.Rmd create mode 100644 02-microarray/gene-id-convert_microarray_01_ensembl.html diff --git a/01-getting-started/getting-started.html b/01-getting-started/getting-started.html index 9b89c5c0..09e70d2c 100644 --- a/01-getting-started/getting-started.html +++ b/01-getting-started/getting-started.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1838,7 +1834,7 @@

    References

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index abac576a..88abe0b5 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1719,7 +1715,7 @@

    CCDL for ALSF

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/02-microarray/clustering_microarray_01_heatmap.html b/02-microarray/clustering_microarray_01_heatmap.html index 95d8977f..09fcc202 100644 --- a/02-microarray/clustering_microarray_01_heatmap.html +++ b/02-microarray/clustering_microarray_01_heatmap.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1901,7 +1897,7 @@

    4.4 Create a heatmap

    ))(25), scale = "row" # Scale values in the direction of genes (rows) ) -

    +

    We’ve created a heatmap but although our genes and samples are clustered, there is not much information that we can gather here because we did not provide the pheatmap() function with annotation labels for our samples.

    First let’s save our clustered heatmap.

    @@ -1918,8 +1914,8 @@

    4.4.1 Save heatmap as a PNG

    # Close the png file: dev.off() -
    ## png 
-##   2
    +
    ## quartz_off_screen 
+##                 2

    Now, let’s add some annotation bars to our heatmap.

    @@ -1963,7 +1959,7 @@

    4.5.1 Create annotated heatmap -

    +

    Now that we have annotation bars on our heatmap, we have a better idea of the cell line and treatment groups that appear to cluster together. More specifically, we can see that the samples seem to cluster by their cell lines of origin, but not necessarily as much by whether or not they received the PLX4302 treatment.

    Let’s save our annotated heatmap.

    @@ -1981,8 +1977,8 @@

    4.5.2 Save annotated heatmap as a # Close the png file: dev.off() -
    ## png 
-##   2
    +
    ## quartz_off_screen 
+##                 2
    @@ -1998,40 +1994,35 @@

    6 Print session info

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.

    # Print session info
 sessionInfo()
    -
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-## [1] magrittr_1.5    pheatmap_1.0.12 optparse_1.6.6 
+## [1] magrittr_1.5    pheatmap_1.0.12 optparse_1.6.2 
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] Rcpp_1.0.5         pillar_1.4.6       compiler_4.0.2     RColorBrewer_1.1-2
-##  [5] R.methodsS3_1.8.0  R.utils_2.9.2      tools_4.0.2        digest_0.6.25     
-##  [9] gtable_0.3.0       evaluate_0.14      lifecycle_0.2.0    tibble_3.0.3      
-## [13] R.cache_0.14.0     pkgconfig_2.0.3    rlang_0.4.7        cli_2.0.2         
-## [17] rstudioapi_0.11    yaml_2.2.1         xfun_0.16          dplyr_1.0.0       
-## [21] styler_1.3.2       stringr_1.4.0      knitr_1.29         generics_0.0.2    
-## [25] vctrs_0.3.2        hms_0.5.3          tidyselect_1.1.0   grid_4.0.2        
-## [29] getopt_1.20.3      glue_1.4.1         R6_2.4.1           fansi_0.4.1       
-## [33] rmarkdown_2.3      farver_2.0.3       purrr_0.3.4        readr_1.3.1       
-## [37] rematch2_2.1.2     scales_1.1.1       backports_1.1.9    ellipsis_0.3.1    
-## [41] htmltools_0.5.0    assertthat_0.2.1   colorspace_1.4-1   utf8_1.1.4        
-## [45] stringi_1.4.6      munsell_0.5.0      crayon_1.3.4       R.oo_1.23.0
    
+##  [1] Rcpp_1.0.4.6       pillar_1.4.4       compiler_3.6.1     RColorBrewer_1.1-2
+##  [5] tools_3.6.1        digest_0.6.25      evaluate_0.14      lifecycle_0.2.0   
+##  [9] tibble_3.0.1       gtable_0.3.0       pkgconfig_2.0.3    rlang_0.4.6       
+## [13] cli_2.0.2          rstudioapi_0.11    yaml_2.2.1         xfun_0.14         
+## [17] dplyr_0.8.3        withr_2.2.0        styler_1.1.1       stringr_1.4.0     
+## [21] knitr_1.28         vctrs_0.3.1        hms_0.5.3          tidyselect_0.2.5  
+## [25] grid_3.6.1         getopt_1.20.3      glue_1.4.1         R6_2.4.1          
+## [29] fansi_0.4.1        rmarkdown_1.14     readr_1.3.1        purrr_0.3.4       
+## [33] rematch2_2.1.0     backports_1.1.7    scales_1.0.0       ellipsis_0.3.1    
+## [37] htmltools_0.3.6    assertthat_0.2.1   colorspace_1.4-1   utf8_1.1.4        
+## [41] stringi_1.4.6      munsell_0.5.0      crayon_1.3.4

    Gu Z., R. Eils, and M. Schlesner, 2016 Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics.

    @@ -2095,7 +2086,7 @@

    6 Print session info

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/02-microarray/dimension-reduction_microarray_01_pca.html b/02-microarray/dimension-reduction_microarray_01_pca.html index c782a21a..16691229 100644 --- a/02-microarray/dimension-reduction_microarray_01_pca.html +++ b/02-microarray/dimension-reduction_microarray_01_pca.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -2179,12 +2175,12 @@

    4.3 Perform Principal Components ## GSM917288 -0.3817511 -0.4095233 -0.6309443 0.5985548 -0.4305740 0.3042307 ## GSM917230 -1.0901163 -2.1937892 -1.9281503 -1.1493521 -0.4783177 0.6360478 ## PC284 PC285 -## GSM917111 9.52572473 -2.012539e-14 -## GSM917250 1.33152530 -1.168683e-14 -## GSM917281 -0.25177843 -1.847134e-14 -## GSM917062 1.11478570 2.137179e-15 -## GSM917288 -0.07702027 -1.411024e-14 -## GSM917230 -1.67762146 -2.067790e-14 +## GSM917111 9.52572473 -4.615319e-14 +## GSM917250 1.33152530 -6.445799e-15 +## GSM917281 -0.25177843 1.736458e-15 +## GSM917062 1.11478570 1.382228e-14 +## GSM917288 -0.07702027 -3.591745e-14 +## GSM917230 -1.67762146 -4.780139e-15

    We can see that we now have 285 principal component values for each of our samples.

    @@ -2236,7 +2232,7 @@

    4.6 Plot PCA Results

    # Print out plot here pca_plot -

    +

    Looks like Group 4 and SHH groups somewhat cluster with each other but Group 3 seems to be less distinct as there are some samples clustering with Group 4 as well.

    We can add another label to our plot to get more information about our dataset. Let’s also label the data points based on the histological subtype that each sample belongs to.

    # Make a scatterplot with ggplot2
@@ -2254,7 +2250,7 @@ 
    4.6 Plot PCA Results
    
 
 # Print out plot here
 pca_plot
    -

    +

    Adding the histological subtype label to our plot made our plot more informative, but the diffuse Group 3 data doesn’t appear to be related to a histology subtype. We could test out other variables as annotation labels to get a further understanding of the cluster behavior of each subgroup.

    @@ -2285,40 +2281,35 @@

    6 Session info

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.

    # Print session info
 sessionInfo()
    -
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-## [1] magrittr_1.5   ggplot2_3.3.2  optparse_1.6.6
+## [1] magrittr_1.5   ggplot2_3.2.1  optparse_1.6.2
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] Rcpp_1.0.5        pillar_1.4.6      compiler_4.0.2    R.methodsS3_1.8.0
-##  [5] R.utils_2.9.2     tools_4.0.2       digest_0.6.25     evaluate_0.14    
-##  [9] lifecycle_0.2.0   tibble_3.0.3      gtable_0.3.0      R.cache_0.14.0   
-## [13] pkgconfig_2.0.3   rlang_0.4.7       cli_2.0.2         rstudioapi_0.11  
-## [17] yaml_2.2.1        xfun_0.16         withr_2.2.0       dplyr_1.0.0      
-## [21] styler_1.3.2      stringr_1.4.0     knitr_1.29        generics_0.0.2   
-## [25] vctrs_0.3.2       hms_0.5.3         tidyselect_1.1.0  grid_4.0.2       
-## [29] getopt_1.20.3     glue_1.4.1        R6_2.4.1          fansi_0.4.1      
-## [33] rmarkdown_2.3     farver_2.0.3      purrr_0.3.4       readr_1.3.1      
-## [37] rematch2_2.1.2    scales_1.1.1      backports_1.1.9   ellipsis_0.3.1   
-## [41] htmltools_0.5.0   assertthat_0.2.1  colorspace_1.4-1  labeling_0.3     
-## [45] stringi_1.4.6     munsell_0.5.0     crayon_1.3.4      R.oo_1.23.0
    
+##  [1] Rcpp_1.0.4.6     pillar_1.4.4     compiler_3.6.1   tools_3.6.1     
+##  [5] digest_0.6.25    evaluate_0.14    lifecycle_0.2.0  tibble_3.0.1    
+##  [9] gtable_0.3.0     pkgconfig_2.0.3  rlang_0.4.6      cli_2.0.2       
+## [13] rstudioapi_0.11  yaml_2.2.1       xfun_0.14        dplyr_0.8.3     
+## [17] withr_2.2.0      styler_1.1.1     stringr_1.4.0    knitr_1.28      
+## [21] vctrs_0.3.1      hms_0.5.3        tidyselect_0.2.5 grid_3.6.1      
+## [25] getopt_1.20.3    glue_1.4.1       R6_2.4.1         fansi_0.4.1     
+## [29] rmarkdown_1.14   readr_1.3.1      purrr_0.3.4      rematch2_2.1.0  
+## [33] backports_1.1.7  scales_1.0.0     ellipsis_0.3.1   htmltools_0.3.6 
+## [37] assertthat_0.2.1 colorspace_1.4-1 labeling_0.3     stringi_1.4.6   
+## [41] lazyeval_0.2.2   munsell_0.5.0    crayon_1.3.4

    Brems M., 2017 A one-stop shop for principal component analysis

    @@ -2391,7 +2382,7 @@

    6 Session info

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/02-microarray/dimension-reduction_microarray_02_umap.html b/02-microarray/dimension-reduction_microarray_02_umap.html index 0629a35e..d7a874af 100644 --- a/02-microarray/dimension-reduction_microarray_02_umap.html +++ b/02-microarray/dimension-reduction_microarray_02_umap.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1868,13 +1864,13 @@

    4.3 Perform Uniform Manifold Appr )

    Let’s take a preview at the data frame we created in the chunk above.

    head(umap_df)
    -
    ##   refinebio_accession_code         X1         X2       histology subgroup
-## 1                GSM917111 3.34604756  0.7845626         Classic  Group 4
-## 2                GSM917250 2.33114827  4.0503256         Classic  Group 4
-## 3                GSM917281 2.52365294  2.4093729         Classic  Group 4
-## 4                GSM917062 0.02614889  1.3860845         Classic  Group 3
-## 5                GSM917288 4.15696395 -1.0232268         Classic  Group 4
-## 6                GSM917230 1.20599505  1.2974269 Medulloblastoma  Group 4
    +
    ##   refinebio_accession_code           X1       X2       histology subgroup
+## 1                GSM917111  0.980478303 3.498827         Classic  Group 4
+## 2                GSM917250  0.007078067 0.164898         Classic  Group 4
+## 3                GSM917281  0.871498571 1.661061         Classic  Group 4
+## 4                GSM917062 -2.389040189 2.977578         Classic  Group 3
+## 5                GSM917288  2.706804399 4.452762         Classic  Group 4
+## 6                GSM917230 -0.750989064 2.847606 Medulloblastoma  Group 4

    Now, let’s plot the UMAP scores.

    # Make a scatterplot using `ggplot2` functionality
 umap_plot <- ggplot(
@@ -1889,7 +1885,7 @@ 
    4.3 Perform Uniform Manifold Appr
 
 # Print out plot here
 umap_plot
    -

    +

    It’s hard to interpret our UMAP results without some metadata labels on our plot.

    Let’s label the data points based on their genotype subgroup since this is central to the subgroup specific based hypothesis in the original paper (Northcott et al. 2012).

    # Make a scatterplot with ggplot2
@@ -1906,7 +1902,7 @@ 
    4.3 Perform Uniform Manifold Appr
 
 # Print out plot here
 umap_plot
    -

    +

    It looks like Group 4 and SHH groups somewhat cluster with each other but Group 3 seems to also be clustering with Group 4.

    We can add another label to our plot to potentially gain insight on the clustering behavior of our data. Let’s also label the data points based on the histological subtype that each sample belongs to.

    # Make a scatterplot with ggplot2
@@ -1924,7 +1920,7 @@ 
    4.3 Perform Uniform Manifold Appr
 
 # Print out plot here
 umap_plot
    -

    +

    Our histological subtype groups don’t appear to be clustering in a discernible pattern. We could test out other variables as annotation labels to get a further understanding of the cluster behavior of each subgroup.

    @@ -1953,42 +1949,37 @@

    6 Session info

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.

    # Print session info
 sessionInfo()
    -
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-## [1] magrittr_1.5   ggplot2_3.3.2  umap_0.2.6.0   optparse_1.6.6
+## [1] magrittr_1.5   ggplot2_3.2.1  umap_0.2.3     optparse_1.6.2
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] reticulate_1.16   styler_1.3.2      tidyselect_1.1.0  xfun_0.16        
-##  [5] rematch2_2.1.2    purrr_0.3.4       lattice_0.20-41   colorspace_1.4-1 
-##  [9] vctrs_0.3.2       generics_0.0.2    htmltools_0.5.0   getopt_1.20.3    
-## [13] yaml_2.2.1        rlang_0.4.7       R.oo_1.23.0       pillar_1.4.6     
-## [17] glue_1.4.1        withr_2.2.0       R.utils_2.9.2     R.cache_0.14.0   
-## [21] lifecycle_0.2.0   stringr_1.4.0     munsell_0.5.0     gtable_0.3.0     
-## [25] R.methodsS3_1.8.0 evaluate_0.14     labeling_0.3      knitr_1.29       
-## [29] fansi_0.4.1       Rcpp_1.0.5        readr_1.3.1       openssl_1.4.2    
-## [33] backports_1.1.9   scales_1.1.1      jsonlite_1.7.0    farver_2.0.3     
-## [37] RSpectra_0.16-0   hms_0.5.3         askpass_1.1       digest_0.6.25    
-## [41] stringi_1.4.6     dplyr_1.0.0       grid_4.0.2        cli_2.0.2        
-## [45] tools_4.0.2       tibble_3.0.3      crayon_1.3.4      pkgconfig_2.0.3  
-## [49] ellipsis_0.3.1    Matrix_1.2-18     assertthat_0.2.1  rmarkdown_2.3    
-## [53] rstudioapi_0.11   R6_2.4.1          compiler_4.0.2
    
+##  [1] Rcpp_1.0.4.6     RSpectra_0.15-0  pillar_1.4.4     compiler_3.6.1  
+##  [5] tools_3.6.1      digest_0.6.25    gtable_0.3.0     jsonlite_1.7.0  
+##  [9] lattice_0.20-38  evaluate_0.14    lifecycle_0.2.0  tibble_3.0.1    
+## [13] pkgconfig_2.0.3  rlang_0.4.6      Matrix_1.2-17    cli_2.0.2       
+## [17] rstudioapi_0.11  yaml_2.2.1       xfun_0.14        dplyr_0.8.3     
+## [21] withr_2.2.0      styler_1.1.1     stringr_1.4.0    knitr_1.28      
+## [25] vctrs_0.3.1      askpass_1.1      hms_0.5.3        tidyselect_0.2.5
+## [29] grid_3.6.1       getopt_1.20.3    reticulate_1.13  glue_1.4.1      
+## [33] R6_2.4.1         fansi_0.4.1      rmarkdown_1.14   readr_1.3.1     
+## [37] purrr_0.3.4      rematch2_2.1.0   scales_1.0.0     backports_1.1.7 
+## [41] ellipsis_0.3.1   htmltools_0.3.6  assertthat_0.2.1 colorspace_1.4-1
+## [45] labeling_0.3     stringi_1.4.6    lazyeval_0.2.2   munsell_0.5.0   
+## [49] openssl_1.4.1    crayon_1.3.4

    Konopka T., 2020 Uniform manifold approximation and projection.

    @@ -2061,7 +2052,7 @@

    6 Session info

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd new file mode 100644 index 00000000..210f0a4b --- /dev/null +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd @@ -0,0 +1,272 @@ +--- +title: "Obtaining Annotation for Ensembl IDs - Microarray" +author: "CCDL for ALSF" +date: "`r format(Sys.time(), '%B %Y')`" +output: + html_notebook: + toc: true + toc_float: true + number_sections: true +--- + +# Purpose of this analysis + +The purpose of this notebook is to provide an example annotation workflow for microarray data obtained from refine.bio using Bioconductor annotation packages. + +⬇️ [**Jump to the analysis code**](#analysis) ⬇️ + +# How to run this example + +For general information about our tutorials and the basic software packages you will need, please see our ['Getting Started' section](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-this-tutorial-is-structured). +We recommend taking a look at our [Resources for Learning R](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#resources-for-learning-r) if you have not written code in R before. + +## Obtain the `.Rmd` file + +To run this example yourself, [download the `.Rmd` for this analysis by clicking this link](https://alexslemonade.github.io/refinebio-examples/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd). + +You can open this `.Rmd` file in RStudio and follow the rest of these steps from there. (See our [section about getting started with R notebooks](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-and-use-rmds) if you are unfamiliar with `.Rmd` files.) +Clicking this link will most likely send this to your downloads folder on your computer. +Move this `.Rmd` file to where you would like this example and its files to be stored. + +## Set up your analysis folders + +Good file organization is helpful for keeping your data analysis project on track! +We have set up some code that will automatically set up a folder structure for you. +Run this next chunk to set up your folders! + +If you have trouble running this chunk, see our [introduction to using `.Rmd`s](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-and-use-rmds) for more resources and explanations. + +```{r} +# Create the data folder if it doesn't exist +if (!dir.exists("data")) { + dir.create("data") +} + +# Define the file path to the plots directory +plots_dir <- "plots" # Can replace with path to desired output plots directory + +# Create the plots folder if it doesn't exist +if (!dir.exists(plots_dir)) { + dir.create(plots_dir) +} + +# Define the file path to the results directory +results_dir <- "results" # Can replace with path to desired output results directory + +# Create the results folder if it doesn't exist +if (!dir.exists(results_dir)) { + dir.create(results_dir) +} +``` + +In the same place you put this `.Rmd` file, you should now have three new empty folders called `data`, `plots`, and `results`! + +## Obtain the dataset from refine.bio + +For general information about downloading data for these examples, see our ['Getting Started' section](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-the-data). + +Go to this [dataset's page on refine.bio]({{REFINEBIO DATASET PAGE LINK}}). + +Click the "Download Now" button on the right side of this screen. + + + +Fill out the pop up window with your email and our Terms and Conditions: + + + + +{{DELETE THIS LINE}} +We are going to use non-quantile normalized data for this analysis. +To get this data, you will need to check the box that says "Skip quantile normalization for RNA-seq samples". +Note that this option will only be available for RNA-seq datasets. + + +{{DELETE THIS LINE}} + +It may take a few minutes for the dataset to process. +You will get an email when it is ready. + +## About the dataset we are using for this example + +For this example analysis, we will use this [mouse glioma stem cells dataset](https://www.refine.bio/experiments/GSE13490/cancer-stem-cells-are-enriched-in-the-side-population-cells-in-a-mouse-model-of-glioma). + + + +## Place the dataset in your new `data/` folder + +refine.bio will send you a download button in the email when it is ready. +Follow the prompt to download a zip file that has a name with a series of letters and numbers and ends in `.zip`. +Double clicking should unzip this for you and create a folder of the same name. + + + +For more details on the contents of this folder see [these docs on refine.bio](http://docs.refine.bio/en/latest/main_text.html#downloadable-files). + +The `` folder has the data and metadata TSV files you will need for this example analysis. +Experiment accession ids usually look something like `GSE1235` or `SRP12345`. + +Copy and paste the `GSE13490` folder into your newly created `data/` folder. + +## Check out our file structure! + +Your new analysis folder should contain: + +- The example analysis Rmd you downloaded +- A folder called "data" which contains: + - The `GSE13490` folder which contains: + - The gene expression + - The metadata TSV +- A folder for `plots` (currently empty) +- A folder for `results` (currently empty) + +Your example analysis folder should now look something like this (except with respective experiment accession id and analysis notebook name you are using): + + + +In order for our example here to run without a hitch, we need these files to be in these locations so we've constructed a test to check before we get started with the analysis. +Run this chunk to double check that your files are in the right place. + +```{r} +# Define the file path to the data directory +data_dir <- file.path("data", "GSE13490") + +# Check if the gene expression matrix file is in the data directory stored as `data_dir` +file.exists(file.path(data_dir, "GSE13490.tsv")) + +# Check if the metadata file is in the data directory stored as `data_dir` +file.exists(file.path(data_dir, "metadata_GSE13490.tsv")) +``` + +If the chunk above printed out `FALSE` to either of those tests, you won't be able to run this analysis _as is_ until those files are in the appropriate place. + +If the concept of a "file path" is unfamiliar to you; we recommend taking a look at our [section about file paths](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#an-important-note-about-file-paths-and-Rmds). + +# Using a different refine.bio dataset with this analysis? + +If you'd like to adapt an example analysis to use a different dataset from [refine.bio](https://www.refine.bio/), we recommend placing the files in the `data/` directory you created and changing the filenames and paths in the notebook to match these files (we've put comments to signify where you would need to change the code). +We suggest saving plots and results to `plots/` and `results/` directories, respectively, as these are automatically created by the notebook. +From here you can customize this analysis example to fit your own scientific questions and preferences. + +*** + +   + + +# {{NAME OF example}} + +Ensembl IDs can be used to obtain various different annotations at the +gene/transcript level. +Although this example script uses Ensembl IDs from Zebrafish, (Danio rerio), +to obtain gene symbols this script can be easily converted for use with different +species or annotation types eg protein IDs, gene ontology, accession +numbers. + +For different species, wherever the abbreviation `org.Dr.eg.db` or `Dr` is +written, it must be replaced with the respective species abbreviation eg +for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. +A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf). + +## Install libraries + +See our Getting Started page with [instructions for package installation](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#what-you-need-to-install) for a list of the other software you will need, as well as more tips and resources. + +In this analysis, we will be using the `org.Mm.eg.db` R package [@Carlson2020]. + +```{r} +# Install the mouse package +if (!("org.Mm.eg.db" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("org.Mm.eg.db", update = FALSE) +} +``` + +Attach the packages we need for this analysis. + +```{r} +# Attach the library +library(org.Mm.eg.db) + +# We will need this so we can use the pipe: %>% +library(magrittr) +``` + +## Import and set up data + +Data downloaded from refine.bio include a metadata tab separated values (TSV) file and a data TSV file. +This chunk of code will read the both TSV files and add them as data frames to your environment. + +```{r} +# Read in metadata TSV file +metadata <- readr::read_tsv(file.path( + data_dir, # Replace with path to your metadata file + "metadata_GSE13490.tsv" # Replace with the name of your metadata file +)) + +# Read in data TSV file +df <- readr::read_tsv(file.path( + data_dir, # Replace with path to your data file + "GSE13490.tsv" # Replace with the name of your data file +)) %>% + # Tuck away the Gene id column as rownames + tibble::column_to_rownames("Gene") +``` + +Let's ensure that the metadata and data are in the same sample order. + +```{r} +# Make the data in the order of the metadata +df <- df %>% + dplyr::select(metadata$geo_accession) + +# Check if this is in the same order +all.equal(colnames(df), metadata$geo_accession) + +# Bring back the "Gene" column in preparation for mapping +df <- df %>% + tibble::rownames_to_column("Gene") +``` + + +## Map Ensembl IDs to gene symbols + +This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect since genes and transcripts do not have a 1:1 relationship. + +```{r} +# Map ensembl IDs to their associated gene symbols +mapped_df <- + data.frame( + "Symbols" = mapIds( + org.Mm.eg.db, + keys = df$Gene, + column = "SYMBOL", + keytype = "ENSEMBL" + ), + df + ) +``` + +## Write to file + +```{r Write to file} +# Write mapped and annotated data frame to output file +readr::write_tsv(mapped_df, file.path( + results_dir, + "GSE13490_Gene_Symbols.tsv" # Replace with a relevant output file name +)) +``` + + +# Resources for further learning + +- [{{HELPFUL RESOURCE NAME}}]({{LINK TO HELPFUL RESOURCE}}) [{{CITATION}}] + +# Session info + +At the end of every analysis, before saving your notebook, we recommend printing out your session info. +This helps make your code more reproducible by recording what versions of softwares and packages you used to run this. + +```{r} +# Print session info +sessionInfo() +``` diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html new file mode 100644 index 00000000..5fe90296 --- /dev/null +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html @@ -0,0 +1,2046 @@ + + + + + + + + + + + + + + + +Obtaining Annotation for Ensembl IDs - Microarray + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
    +
    +
    +
    +
    + +
    + + + + + + + + + +
    +

    1 Purpose of this analysis

    +

    The purpose of this notebook is to provide an example annotation workflow for microarray data obtained from refine.bio using Bioconductor annotation packages.

    +

    ⬇️ Jump to the analysis code ⬇️

    +
    +
    +

    2 How to run this example

    +

    For general information about our tutorials and the basic software packages you will need, please see our ‘Getting Started’ section. We recommend taking a look at our Resources for Learning R if you have not written code in R before.

    +
    +

    2.1 Obtain the .Rmd file

    +

    To run this example yourself, download the .Rmd for this analysis by clicking this link.

    +

    You can open this .Rmd file in RStudio and follow the rest of these steps from there. (See our section about getting started with R notebooks if you are unfamiliar with .Rmd files.) Clicking this link will most likely send this to your downloads folder on your computer. Move this .Rmd file to where you would like this example and its files to be stored.

    +
    +
    +

    2.2 Set up your analysis folders

    +

    Good file organization is helpful for keeping your data analysis project on track! We have set up some code that will automatically set up a folder structure for you. Run this next chunk to set up your folders!

    +

    If you have trouble running this chunk, see our introduction to using .Rmds for more resources and explanations.

    +
    # Create the data folder if it doesn't exist
+if (!dir.exists("data")) {
+  dir.create("data")
+}
+
+# Define the file path to the plots directory
+plots_dir <- "plots" # Can replace with path to desired output plots directory
+
+# Create the plots folder if it doesn't exist
+if (!dir.exists(plots_dir)) {
+  dir.create(plots_dir)
+}
+
+# Define the file path to the results directory
+results_dir <- "results" # Can replace with path to desired output results directory
+
+# Create the results folder if it doesn't exist
+if (!dir.exists(results_dir)) {
+  dir.create(results_dir)
+}
    +

    In the same place you put this .Rmd file, you should now have three new empty folders called data, plots, and results!

    +
    +
    +

    2.3 Obtain the dataset from refine.bio

    +

    For general information about downloading data for these examples, see our ‘Getting Started’ section.

    +

    Go to this dataset’s page on refine.bio.

    +

    Click the “Download Now” button on the right side of this screen.

    +

    +

    Fill out the pop up window with your email and our Terms and Conditions:

    +

    +

    {{DELETE THIS LINE}} We are going to use non-quantile normalized data for this analysis. To get this data, you will need to check the box that says “Skip quantile normalization for RNA-seq samples”. Note that this option will only be available for RNA-seq datasets.

    +

    {{DELETE THIS LINE}}

    +

    It may take a few minutes for the dataset to process. You will get an email when it is ready.

    +
    +
    +

    2.4 About the dataset we are using for this example

    +

    For this example analysis, we will use this mouse glioma stem cells dataset.

    + +
    +
    +

    2.5 Place the dataset in your new data/ folder

    +

    refine.bio will send you a download button in the email when it is ready. Follow the prompt to download a zip file that has a name with a series of letters and numbers and ends in .zip. Double clicking should unzip this for you and create a folder of the same name.

    +

    +

    For more details on the contents of this folder see these docs on refine.bio.

    +

    The <experiment_accession_id> folder has the data and metadata TSV files you will need for this example analysis. Experiment accession ids usually look something like GSE1235 or SRP12345.

    +

    Copy and paste the GSE13490 folder into your newly created data/ folder.

    +
    +
    +

    2.6 Check out our file structure!

    +

    Your new analysis folder should contain:

    +
      +
    • The example analysis Rmd you downloaded
      +
      • +
    • A folder called “data” which contains: +
        +
      • The GSE13490 folder which contains: +
          +
        • The gene expression
          +
          • +
        • The metadata TSV
          +
          • +
        • +
      • +
    • A folder for plots (currently empty)
      +
      • +
    • A folder for results (currently empty)
      • +
    +

    Your example analysis folder should now look something like this (except with respective experiment accession id and analysis notebook name you are using):

    +

    +

    In order for our example here to run without a hitch, we need these files to be in these locations so we’ve constructed a test to check before we get started with the analysis. Run this chunk to double check that your files are in the right place.

    +
    # Define the file path to the data directory
+data_dir <- file.path("data", "GSE13490")
+
+# Check if the gene expression matrix file is in the data directory stored as `data_dir`
+file.exists(file.path(data_dir, "GSE13490.tsv"))
    +
    ## [1] TRUE
    +
    # Check if the metadata file is in the data directory stored as `data_dir`
+file.exists(file.path(data_dir, "metadata_GSE13490.tsv"))
    +
    ## [1] TRUE
    +

    If the chunk above printed out FALSE to either of those tests, you won’t be able to run this analysis as is until those files are in the appropriate place.

    +

    If the concept of a “file path” is unfamiliar to you; we recommend taking a look at our section about file paths.

    +
    +
    +
    +

    3 Using a different refine.bio dataset with this analysis?

    +

    If you’d like to adapt an example analysis to use a different dataset from refine.bio, we recommend placing the files in the data/ directory you created and changing the filenames and paths in the notebook to match these files (we’ve put comments to signify where you would need to change the code). We suggest saving plots and results to plots/ and results/ directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences.

    +
    + +

     

    +
    +
    +

    4 {{NAME OF example}}

    +

    Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Although this example script uses Ensembl IDs from Zebrafish, (Danio rerio), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

    +

    For different species, wherever the abbreviation org.Dr.eg.db or Dr is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link.

    +
    +

    4.1 Install libraries

    +

    See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.

    +

    In this analysis, we will be using the org.Mm.eg.db R package (Carlson).

    +
    # Install the mouse package
+if (!("org.Mm.eg.db" %in% installed.packages())) {
+  # Install this package if it isn't installed yet
+  BiocManager::install("org.Mm.eg.db", update = FALSE)
+}
    +

    Attach the packages we need for this analysis.

    +
    # Attach the library
+library(org.Mm.eg.db)
    +
    ## Loading required package: AnnotationDbi
    +
    ## Loading required package: stats4
    +
    ## Loading required package: BiocGenerics
    +
    ## Loading required package: parallel
    +
    ## 
+## Attaching package: 'BiocGenerics'
    +
    ## The following objects are masked from 'package:parallel':
+## 
+##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
+##     clusterExport, clusterMap, parApply, parCapply, parLapply,
+##     parLapplyLB, parRapply, parSapply, parSapplyLB
    +
    ## The following objects are masked from 'package:stats':
+## 
+##     IQR, mad, sd, var, xtabs
    +
    ## The following objects are masked from 'package:base':
+## 
+##     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
+##     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
+##     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
+##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
+##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
+##     union, unique, unsplit, which, which.max, which.min
    +
    ## Loading required package: Biobase
    +
    ## Welcome to Bioconductor
+## 
+##     Vignettes contain introductory material; view with
+##     'browseVignettes()'. To cite Bioconductor, see
+##     'citation("Biobase")', and for packages 'citation("pkgname")'.
    +
    ## Loading required package: IRanges
    +
    ## Loading required package: S4Vectors
    +
    ## 
+## Attaching package: 'S4Vectors'
    +
    ## The following object is masked from 'package:base':
+## 
+##     expand.grid
    +
    ##
    +
    # We will need this so we can use the pipe: %>%
+library(magrittr)
    +
    +
    +

    4.2 Import and set up data

    +

    Data downloaded from refine.bio include a metadata tab separated values (TSV) file and a data TSV file. This chunk of code will read the both TSV files and add them as data frames to your environment.

    +
    # Read in metadata TSV file
+metadata <- readr::read_tsv(file.path(
+  data_dir, # Replace with path to your metadata file
+  "metadata_GSE13490.tsv" # Replace with the name of your metadata file
+))
    +
    ## Parsed with column specification:
+## cols(
+##   .default = col_character(),
+##   refinebio_age = col_logical(),
+##   refinebio_cell_line = col_logical(),
+##   refinebio_compound = col_logical(),
+##   refinebio_disease = col_logical(),
+##   refinebio_disease_stage = col_logical(),
+##   refinebio_genetic_information = col_logical(),
+##   refinebio_processed = col_logical(),
+##   refinebio_race = col_logical(),
+##   refinebio_sex = col_logical(),
+##   refinebio_source_archive_url = col_logical(),
+##   refinebio_specimen_part = col_logical(),
+##   refinebio_subject = col_logical(),
+##   refinebio_time = col_logical(),
+##   refinebio_treatment = col_logical(),
+##   channel_count = col_double(),
+##   data_row_count = col_double(),
+##   taxid_ch1 = col_double()
+## )
    +
    ## See spec(...) for full column specifications.
    +
    # Read in data TSV file
+df <- readr::read_tsv(file.path(
+  data_dir, # Replace with path to your data file
+  "GSE13490.tsv" # Replace with the name of your data file
+)) %>%
+  # Tuck away the Gene id column as rownames
+  tibble::column_to_rownames("Gene")
    +
    ## Parsed with column specification:
+## cols(
+##   Gene = col_character(),
+##   GSM340064 = col_double(),
+##   GSM340065 = col_double(),
+##   GSM340066 = col_double(),
+##   GSM340067 = col_double(),
+##   GSM340068 = col_double(),
+##   GSM340069 = col_double(),
+##   GSM340070 = col_double(),
+##   GSM340071 = col_double(),
+##   GSM340072 = col_double(),
+##   GSM340073 = col_double(),
+##   GSM340074 = col_double(),
+##   GSM340075 = col_double(),
+##   GSM340076 = col_double(),
+##   GSM340077 = col_double(),
+##   GSM340078 = col_double()
+## )
    +

    Let’s ensure that the metadata and data are in the same sample order.

    +
    # Make the data in the order of the metadata
+df <- df %>%
+  dplyr::select(metadata$geo_accession)
+
+# Check if this is in the same order
+all.equal(colnames(df), metadata$geo_accession)
    +
    ## [1] TRUE
    +
    # Bring back the "Gene" column in preparation for mapping
+df <- df %>%
+  tibble::rownames_to_column("Gene")
    +
    +
    +

    4.3 Map Ensembl IDs to gene symbols

    +

    This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect since genes and transcripts do not have a 1:1 relationship.

    +
    # Map ensembl IDs to their associated gene symbols
+mapped_df <-
+  data.frame(
+    "Symbols" = mapIds(
+      org.Mm.eg.db,
+      keys = df$Gene,
+      column = "SYMBOL",
+      keytype = "ENSEMBL"
+    ),
+    df
+  )
    +
    ## 'select()' returned 1:many mapping between keys and columns
    +
    +
    +

    4.4 Write to file

    +
    # Write mapped and annotated data frame to output file
+readr::write_tsv(mapped_df, file.path(
+  results_dir,
+  "GSE13490_Gene_Symbols.tsv" # Replace with a relevant output file name
+))
    +
    +
    +
    +

    5 Resources for further learning

    + +
    +
    +

    6 Session info

    +

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.

    +
    # Print session info
+sessionInfo()
    +
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
+## 
+## Matrix products: default
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
+## 
+## locale:
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+## 
+## attached base packages:
+## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
+## [8] methods   base     
+## 
+## other attached packages:
+## [1] magrittr_1.5         org.Mm.eg.db_3.8.2   AnnotationDbi_1.47.0
+## [4] IRanges_2.19.10      S4Vectors_0.23.23    Biobase_2.45.0      
+## [7] BiocGenerics_0.31.5  optparse_1.6.2      
+## 
+## loaded via a namespace (and not attached):
+##  [1] Rcpp_1.0.4.6     pillar_1.4.4     compiler_3.6.1   tools_3.6.1     
+##  [5] bit_1.1-14       digest_0.6.25    memoise_1.1.0    RSQLite_2.1.2   
+##  [9] evaluate_0.14    lifecycle_0.2.0  tibble_3.0.1     pkgconfig_2.0.3 
+## [13] rlang_0.4.6      DBI_1.0.0        cli_2.0.2        rstudioapi_0.11 
+## [17] yaml_2.2.1       xfun_0.14        dplyr_0.8.3      withr_2.2.0     
+## [21] styler_1.1.1     stringr_1.4.0    knitr_1.28       vctrs_0.3.1     
+## [25] hms_0.5.3        tidyselect_0.2.5 bit64_0.9-7      getopt_1.20.3   
+## [29] glue_1.4.1       R6_2.4.1         fansi_0.4.1      rmarkdown_1.14  
+## [33] blob_1.2.0       readr_1.3.1      purrr_0.3.4      rematch2_2.1.0  
+## [37] backports_1.1.7  ellipsis_0.3.1   htmltools_0.3.6  assertthat_0.2.1
+## [41] stringi_1.4.6    crayon_1.3.4
    +
    +
    +

    Carlson M., Genome wide annotation for mouse

    +
    +
    +
    + + + +
    +
    + +
    + + + + + + + + + + + + + + + + diff --git a/03-rnaseq/00-intro-to-rnaseq.html b/03-rnaseq/00-intro-to-rnaseq.html index 6d34aecc..bf0d9da7 100644 --- a/03-rnaseq/00-intro-to-rnaseq.html +++ b/03-rnaseq/00-intro-to-rnaseq.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1719,7 +1715,7 @@

    CCDL for ALSF

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/03-rnaseq/clustering_rnaseq_01_heatmap.html b/03-rnaseq/clustering_rnaseq_01_heatmap.html index 75d06b37..3d1d8d41 100644 --- a/03-rnaseq/clustering_rnaseq_01_heatmap.html +++ b/03-rnaseq/clustering_rnaseq_01_heatmap.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1839,6 +1835,7 @@

    4.1 Install libraries

    ## The following objects are masked from 'package:Biobase':
 ## 
 ##     anyMissing, rowMedians
    +
    ## Loading required package: BiocParallel
    ## 
 ## Attaching package: 'DelayedArray'
    ## The following objects are masked from 'package:matrixStats':
@@ -1893,12 +1890,12 @@ 
    4.2 Import and set up data
    
 
    ## # A tibble: 6 x 20
 ##   refinebio_acces… experiment_acce… refinebio_age refinebio_cell_…
 ##   <chr>            <chr>            <lgl>         <lgl>           
-## 1 SRR3189694       SRP070849        NA            NA              
-## 2 SRR3189680       SRP070849        NA            NA              
-## 3 SRR3189690       SRP070849        NA            NA              
-## 4 SRR3189684       SRP070849        NA            NA              
-## 5 SRR3189683       SRP070849        NA            NA              
-## 6 SRR3189691       SRP070849        NA            NA              
+## 1 SRR3189684       SRP070849        NA            NA              
+## 2 SRR3189690       SRP070849        NA            NA              
+## 3 SRR3189691       SRP070849        NA            NA              
+## 4 SRR3189683       SRP070849        NA            NA              
+## 5 SRR3189693       SRP070849        NA            NA              
+## 6 SRR3189692       SRP070849        NA            NA              
 ## # … with 16 more variables: refinebio_compound <lgl>, refinebio_disease <lgl>,
 ## #   refinebio_disease_stage <lgl>, refinebio_genetic_information <lgl>,
 ## #   refinebio_organism <chr>, refinebio_platform <chr>,
@@ -1977,7 +1974,7 @@ 
    4.7 Create a heatmap
    
     ),
     scale = "row" # Scale values in the direction of genes (rows)
   )
    
-
    
+
    
 
    We’ve created a heatmap but although our genes and samples are clustered, there is not much information that we can gather here because we did not provide the pheatmap() function with annotation labels for our samples.
    
 
    First let’s save our clustered heatmap.
    
 
    
@@ -1994,8 +1991,8 @@ 
    4.7.1 Save heatmap as a png
    
 
 # Close the png file:
 dev.off()
    -
    ## png 
-##   2
    +
    ## quartz_off_screen 
+##                 2

    Now, let’s add some annotation bars to our heatmap.

    @@ -2041,7 +2038,7 @@

    4.8.1 Create annotated heatmap -

    +

    Now that we have annotation bars on our heatmap, we have a better idea of the sample variable groups that appear to cluster together.

    Let’s save our annotated heatmap.

    @@ -2059,8 +2056,8 @@

    4.8.2 Save annotated heatmap as a # Close the png file: dev.off() -
    ## png 
-##   2
    +
    ## quartz_off_screen 
+##                 2

    @@ -2076,59 +2073,58 @@

    6 Session info

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.

    # Print session info
 sessionInfo()
    -
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
 ## [8] methods   base     
 ## 
 ## other attached packages:
-##  [1] magrittr_1.5                DESeq2_1.28.1              
-##  [3] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
-##  [5] matrixStats_0.56.0          Biobase_2.48.0             
-##  [7] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
-##  [9] IRanges_2.22.2              S4Vectors_0.26.1           
-## [11] BiocGenerics_0.34.0         pheatmap_1.0.12            
-## [13] optparse_1.6.6             
+##  [1] magrittr_1.5                DESeq2_1.26.0              
+##  [3] SummarizedExperiment_1.15.6 DelayedArray_0.11.4        
+##  [5] BiocParallel_1.19.2         matrixStats_0.54.0         
+##  [7] Biobase_2.45.0              GenomicRanges_1.37.14      
+##  [9] GenomeInfoDb_1.21.1         IRanges_2.19.10            
+## [11] S4Vectors_0.23.23           BiocGenerics_0.31.5        
+## [13] pheatmap_1.0.12             optparse_1.6.2             
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] bit64_0.9-7.1          splines_4.0.2          R.utils_2.9.2         
-##  [4] assertthat_0.2.1       blob_1.2.1             GenomeInfoDbData_1.2.3
-##  [7] yaml_2.2.1             pillar_1.4.6           RSQLite_2.2.0         
-## [10] backports_1.1.9        lattice_0.20-41        glue_1.4.1            
-## [13] digest_0.6.25          RColorBrewer_1.1-2     XVector_0.28.0        
-## [16] colorspace_1.4-1       htmltools_0.5.0        Matrix_1.2-18         
-## [19] R.oo_1.23.0            XML_3.99-0.5           pkgconfig_2.0.3       
-## [22] genefilter_1.70.0      zlibbioc_1.34.0        purrr_0.3.4           
-## [25] xtable_1.8-4           scales_1.1.1           getopt_1.20.3         
-## [28] BiocParallel_1.22.0    tibble_3.0.3           annotate_1.66.0       
-## [31] styler_1.3.2           farver_2.0.3           generics_0.0.2        
-## [34] ggplot2_3.3.2          ellipsis_0.3.1         cli_2.0.2             
-## [37] survival_3.1-12        crayon_1.3.4           memoise_1.1.0         
-## [40] evaluate_0.14          R.methodsS3_1.8.0      fansi_0.4.1           
-## [43] R.cache_0.14.0         tools_4.0.2            hms_0.5.3             
-## [46] lifecycle_0.2.0        stringr_1.4.0          locfit_1.5-9.4        
-## [49] munsell_0.5.0          AnnotationDbi_1.50.3   compiler_4.0.2        
-## [52] rlang_0.4.7            grid_4.0.2             RCurl_1.98-1.2        
-## [55] rstudioapi_0.11        bitops_1.0-6           rmarkdown_2.3         
-## [58] gtable_0.3.0           DBI_1.1.0              rematch2_2.1.2        
-## [61] R6_2.4.1               knitr_1.29             dplyr_1.0.0           
-## [64] utf8_1.1.4             bit_1.1-15.2           readr_1.3.1           
-## [67] stringi_1.4.6          Rcpp_1.0.5             vctrs_0.3.2           
-## [70] geneplotter_1.66.0     tidyselect_1.1.0       xfun_0.16
    
+##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
+##  [4] tools_3.6.1            backports_1.1.7        utf8_1.1.4            
+##  [7] R6_2.4.1               rpart_4.1-15           Hmisc_4.2-0           
+## [10] DBI_1.0.0              lazyeval_0.2.2         colorspace_1.4-1      
+## [13] nnet_7.3-12            withr_2.2.0            tidyselect_0.2.5      
+## [16] gridExtra_2.3          bit_1.1-14             compiler_3.6.1        
+## [19] cli_2.0.2              htmlTable_1.13.1       scales_1.0.0          
+## [22] checkmate_1.9.4        readr_1.3.1            genefilter_1.67.1     
+## [25] stringr_1.4.0          digest_0.6.25          foreign_0.8-72        
+## [28] rmarkdown_1.14         XVector_0.25.0         base64enc_0.1-3       
+## [31] pkgconfig_2.0.3        htmltools_0.3.6        styler_1.1.1          
+## [34] htmlwidgets_1.3        rlang_0.4.6            rstudioapi_0.11       
+## [37] RSQLite_2.1.2          acepack_1.4.1          dplyr_0.8.3           
+## [40] RCurl_1.95-4.12        GenomeInfoDbData_1.2.1 Formula_1.2-3         
+## [43] Matrix_1.2-17          Rcpp_1.0.4.6           munsell_0.5.0         
+## [46] fansi_0.4.1            lifecycle_0.2.0        stringi_1.4.6         
+## [49] yaml_2.2.1             zlibbioc_1.31.0        grid_3.6.1            
+## [52] blob_1.2.0             crayon_1.3.4           lattice_0.20-38       
+## [55] splines_3.6.1          annotate_1.63.0        hms_0.5.3             
+## [58] locfit_1.5-9.1         knitr_1.28             pillar_1.4.4          
+## [61] geneplotter_1.63.0     XML_3.98-1.20          glue_1.4.1            
+## [64] evaluate_0.14          latticeExtra_0.6-28    data.table_1.12.2     
+## [67] vctrs_0.3.1            gtable_0.3.0           getopt_1.20.3         
+## [70] purrr_0.3.4            rematch2_2.1.0         assertthat_0.2.1      
+## [73] ggplot2_3.2.1          xfun_0.14              xtable_1.8-4          
+## [76] survival_2.44-1.1      tibble_3.0.1           memoise_1.1.0         
+## [79] AnnotationDbi_1.47.0   cluster_2.1.0          ellipsis_0.3.1

    Gu Z., R. Eils, and M. Schlesner, 2016 Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics.

    @@ -2195,7 +2191,7 @@

    6 Session info

    theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/03-rnaseq/differential-expression_rnaseq_01.html b/03-rnaseq/differential-expression_rnaseq_01.html index a2975f9f..134f9459 100644 --- a/03-rnaseq/differential-expression_rnaseq_01.html +++ b/03-rnaseq/differential-expression_rnaseq_01.html @@ -1,10 +1,11 @@ - + + @@ -1343,6 +1344,7 @@ } img { max-width:100%; + height: auto; } .tabbed-pane { padding-top: 12px; @@ -1491,7 +1493,6 @@ border: none; display: inline-block; border-radius: 4px; - background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1520,12 +1521,6 @@ } } -@media print { -.toc-content { - /* see https://github.com/w3c/csswg-drafts/issues/4434 */ - float: right; -} -} .toc-content { padding-left: 30px; @@ -1619,6 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Gene ID Conversion
    • @@ -1839,6 +1835,7 @@

    4.1 Install libraries

    ## The following objects are masked from 'package:Biobase':
 ## 
 ##     anyMissing, rowMedians
    +
    ## Loading required package: BiocParallel
    ## 
 ## Attaching package: 'DelayedArray'
    ## The following objects are masked from 'package:matrixStats':
@@ -1910,8 +1907,8 @@ 
    4.2 Import data and metadata
    
 
    ## [1] TRUE
    
 
    The information we need to make the comparison is in the refinebio_title column of the metadata data.frame.
    
 
    head(metadata$refinebio_title)
    
-
    ## [1] "CBF54-BM-ASXLwt"      "49060-2010-BM-ASXLwt" "CBF234-BM-ASXLwt"    
-## [4] "CBF124-BM-ASXLwt"     "41267-BM-ASXL2"       "45565-BM-ASXL1"
    
+
    ## [1] "CBF369-Blood-ASXL2"   "41267-BM-ASXL2"       "CBF234-BM-ASXLwt"    
+## [4] "40810-BM-ASXLwt"      "49060-2010-BM-ASXLwt" "CBF77-Blood-ASXL2"

    4.3 Set up metadata

    @@ -1935,16 +1932,16 @@

    4.3 Set up metadata

    ## # A tibble: 6 x 2
 ##   refinebio_title      asxl_mutation_status
 ##   <chr>                <chr>               
-## 1 CBF54-BM-ASXLwt      no_mutation         
-## 2 49060-2010-BM-ASXLwt no_mutation         
+## 1 CBF369-Blood-ASXL2   asxl_mutation       
+## 2 41267-BM-ASXL2       asxl_mutation       
 ## 3 CBF234-BM-ASXLwt     no_mutation         
-## 4 CBF124-BM-ASXLwt     no_mutation         
-## 5 41267-BM-ASXL2       asxl_mutation       
-## 6 45565-BM-ASXL1       asxl_mutation
    +## 4 40810-BM-ASXLwt no_mutation +## 5 49060-2010-BM-ASXLwt no_mutation +## 6 CBF77-Blood-ASXL2 asxl_mutation

    Before we set up our model in the next step, we want to check if our modeling variable is set correctly. We want our “control” to to be set as the first level in the variable we provide as our experimental variable. Here we will use the str() function to print out a preview of the structure of our variable

    # Print out a preview of `asxl_mutation_status`
 str(metadata$asxl_mutation_status)
    -
    ##  chr [1:16] "no_mutation" "no_mutation" "no_mutation" "no_mutation" ...
    +
    ##  chr [1:16] "asxl_mutation" "asxl_mutation" "no_mutation" "no_mutation" ...

    Currently, asxl_mutation_status is a character. To make sure it is set how we want for the DESeq object and subsequent testing, let’s mutate it to a factor so we can explicitly set the levels.

    # Make asxl_mutation_status a factor and set the levels appropriately
 metadata <- metadata %>%
@@ -2000,23 +1997,32 @@ 
    4.6 Run differential expression a
   coef = 2, # This is based on what log fold change coefficient was used in DESeq(), the default is 2.
   res = deseq_results # This needs to be the DESeq results table
 )
    -
    ## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
-##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
-##     sequence count data: removing the noise and preserving large differences.
-##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
    +
    ## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
+## 
+## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
+## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
+## Reference: https://doi.org/10.1093/bioinformatics/bty895

    Now let’s take a peek at what our results table looks like.

    head(deseq_results)
    ## log2 fold change (MAP): asxl mutation status asxl mutation vs no mutation 
 ## Wald test p-value: asxl mutation status asxl mutation vs no mutation 
-## DataFrame with 6 rows and 5 columns
-##                    baseMean log2FoldChange      lfcSE    pvalue      padj
-##                   <numeric>      <numeric>  <numeric> <numeric> <numeric>
-## ENSG00000000003   52.852059    9.64776e-07 0.00144269 0.6604581  0.998525
-## ENSG00000000005    0.260056    2.03308e-07 0.00144269 0.5791283        NA
-## ENSG00000000419  406.161355   -1.38160e-06 0.00144268 0.7076357  0.998525
-## ENSG00000000457  564.784021   -2.36497e-06 0.00144268 0.3719972  0.998525
-## ENSG00000000460  401.130684    3.87280e-06 0.00144269 0.1627644  0.998525
-## ENSG00000000938 1500.527448    3.07435e-06 0.00144269 0.0354596  0.720601
    +## DataFrame with 6 rows and 6 columns +## baseMean log2FoldChange lfcSE +## <numeric> <numeric> <numeric> +## ENSG00000000003 52.8520587655854 0.12593859821352 0.292427834964723 +## ENSG00000000005 0.260055561421088 0.0412286839932318 0.143323523204828 +## ENSG00000000419 406.161355152407 -0.0872933065549999 0.232613912574037 +## ENSG00000000457 564.784020901503 -0.239724224515893 0.26943713590588 +## ENSG00000000460 401.130684469072 0.373394326554735 0.267898911424576 +## ENSG00000000938 1500.52744835278 0.623523965932518 0.304354167130984 +## stat pvalue padj +## <numeric> <numeric> <numeric> +## ENSG00000000003 0.439280729657335 0.660458135481068 0.998525105718721 +## ENSG00000000005 -0.554658424129523 0.579128318966208 NA +## ENSG00000000419 -0.375033246094286 0.707635741349663 0.998525105718721 +## ENSG00000000457 -0.892738568914621 0.371997190749564 0.998525105718721 +## ENSG00000000460 1.39583439943176 0.162764368904366 0.998525105718721 +## ENSG00000000938 2.10307054824782 0.0354596030191536 0.720600898549108

    Note it is not filtered or sorted, so we will use tidyverse to do this before saving our results to a file. Sort and filter the results.

    # this is of class DESeqResults -- we want a data frame
 deseq_df <- deseq_results %>%
@@ -2031,25 +2037,25 @@ 
    4.6 Run differential expression a
   dplyr::arrange(dplyr::desc(log2FoldChange))

    Let’s print out what the top results are.

    head(deseq_df)
    -
    ##              Gene   baseMean log2FoldChange     lfcSE       pvalue      padj
-## 1 ENSG00000049247  19.204037       6.174579 2.2940036 0.0167489785 0.5761216
-## 2 ENSG00000162551 179.261210       5.824919 1.2427084 0.0008553285 0.1690009
-## 3 ENSG00000273079  26.113327       4.172007 0.7078167 0.0203873911 0.6263925
-## 4 ENSG00000148926  96.538019       3.952953 0.8327353 0.0284780003 0.6768306
-## 5 ENSG00000251139   5.018181       3.867049 0.8624662 0.0270431735        NA
-## 6 ENSG00000108244  17.988423       3.731020 0.9987549 0.0012635570 0.2025067
-##   threshold
-## 1     FALSE
-## 2     FALSE
-## 3     FALSE
-## 4     FALSE
-## 5        NA
-## 6     FALSE
    +
    ##              Gene    baseMean log2FoldChange     lfcSE       stat       pvalue
+## 1 ENSG00000115590  152.279126       1.873505 0.3037832  3.3030575 0.0009563678
+## 2 ENSG00000273079   26.113327       1.687493 0.3045453  2.3191410 0.0203873911
+## 3 ENSG00000154589   51.259590       1.686259 0.3031344  1.9123102 0.0558364219
+## 4 ENSG00000164588    9.276832       1.659394 0.2705837 -0.3576455 0.7206086031
+## 5 ENSG00000103569 1218.109594       1.636066 0.3035410  3.3306457 0.0008664481
+## 6 ENSG00000244115   92.694075       1.571892 0.2753955  1.3663784 0.1718202238
+##        padj threshold
+## 1 0.1782265     FALSE
+## 2 0.6263925     FALSE
+## 3 0.8208543     FALSE
+## 4        NA        NA
+## 5 0.1690009     FALSE
+## 6 0.9985251     FALSE

    4.6.1 Check results by plotting one gene

    To double check what a differentially expressed gene looks like, we can plot one with DESeq2::plotCounts() function.

    plotCounts(ddset, gene = "ENSG00000196074", intgroup = "asxl_mutation_status")
    -

    +

    The mutation group samples have higher expression of this gene than the control group, which helps assure us that the results are showing us what we are looking for.

    @@ -2073,7 +2079,7 @@

    4.8 Create a volcano plot

    x = "log2FoldChange", # The variable in `deseq_df` you want to be plotted on the x axis y = "padj" # The variable in `deseq_df` you want to be plotted on the y axis ) -

    +

    Here the red point is the gene that meets both the default p value and log2 fold change cutoff (which are 10e-6 and 1 respectively).

    We used the adjusted p values for our plot above, so you may want to loosen this cutoff with the pCutoff argument (Take a look at all the options for tailoring this plot using ?EnhancedVolcano).

    Let’s make the same plot again, but adjust the pCutoff since we are using multiple-testing corrected p values and this time we will assign the plot to our environment as volcano_plot.

    @@ -2088,7 +2094,7 @@

    4.8 Create a volcano plot

    # Print out plot here volcano_plot -

    +

    This looks pretty good. Let’s save it to a PNG.

    ggsave(
   plot = volcano_plot,
@@ -2112,63 +2118,59 @@ 
    6 Session info
    
 
    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.
    
 
    # Print session info
 sessionInfo()
    
-
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
 ## [8] methods   base     
 ## 
 ## other attached packages:
-##  [1] magrittr_1.5                ggplot2_3.3.2              
-##  [3] DESeq2_1.28.1               SummarizedExperiment_1.18.2
-##  [5] DelayedArray_0.14.1         matrixStats_0.56.0         
-##  [7] Biobase_2.48.0              GenomicRanges_1.40.0       
-##  [9] GenomeInfoDb_1.24.2         IRanges_2.22.2             
-## [11] S4Vectors_0.26.1            BiocGenerics_0.34.0        
-## [13] optparse_1.6.6             
+##  [1] magrittr_1.5                ggplot2_3.2.1              
+##  [3] DESeq2_1.26.0               SummarizedExperiment_1.15.6
+##  [5] DelayedArray_0.11.4         BiocParallel_1.19.2        
+##  [7] matrixStats_0.54.0          Biobase_2.45.0             
+##  [9] GenomicRanges_1.37.14       GenomeInfoDb_1.21.1        
+## [11] IRanges_2.19.10             S4Vectors_0.23.23          
+## [13] BiocGenerics_0.31.5         optparse_1.6.2             
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] bitops_1.0-6           bit64_0.9-7.1          RColorBrewer_1.1-2    
-##  [4] numDeriv_2016.8-1.1    R.cache_0.14.0         tools_4.0.2           
-##  [7] backports_1.1.9        utf8_1.1.4             R6_2.4.1              
-## [10] DBI_1.1.0              colorspace_1.4-1       apeglm_1.10.0         
-## [13] withr_2.2.0            tidyselect_1.1.0       bit_1.1-15.2          
-## [16] compiler_4.0.2         cli_2.0.2              labeling_0.3          
-## [19] scales_1.1.1           mvtnorm_1.1-1          readr_1.3.1           
-## [22] genefilter_1.70.0      stringr_1.4.0          digest_0.6.25         
-## [25] rmarkdown_2.3          R.utils_2.9.2          XVector_0.28.0        
-## [28] pkgconfig_2.0.3        htmltools_0.5.0        styler_1.3.2          
-## [31] bbmle_1.0.23.1         rlang_0.4.7            rstudioapi_0.11       
-## [34] RSQLite_2.2.0          farver_2.0.3           generics_0.0.2        
-## [37] BiocParallel_1.22.0    dplyr_1.0.0            R.oo_1.23.0           
-## [40] RCurl_1.98-1.2         GenomeInfoDbData_1.2.3 Matrix_1.2-18         
-## [43] Rcpp_1.0.5             munsell_0.5.0          fansi_0.4.1           
-## [46] lifecycle_0.2.0        R.methodsS3_1.8.0      stringi_1.4.6         
-## [49] yaml_2.2.1             MASS_7.3-51.6          zlibbioc_1.34.0       
-## [52] plyr_1.8.6             grid_4.0.2             blob_1.2.1            
-## [55] ggrepel_0.8.2          bdsmatrix_1.3-4        crayon_1.3.4          
-## [58] lattice_0.20-41        splines_4.0.2          annotate_1.66.0       
-## [61] hms_0.5.3              locfit_1.5-9.4         knitr_1.29            
-## [64] pillar_1.4.6           geneplotter_1.66.0     XML_3.99-0.5          
-## [67] glue_1.4.1             evaluate_0.14          EnhancedVolcano_1.6.0 
-## [70] vctrs_0.3.2            gtable_0.3.0           getopt_1.20.3         
-## [73] purrr_0.3.4            rematch2_2.1.2         assertthat_0.2.1      
-## [76] emdbook_1.3.12         xfun_0.16              xtable_1.8-4          
-## [79] coda_0.19-3            survival_3.1-12        tibble_3.0.3          
-## [82] AnnotationDbi_1.50.3   memoise_1.1.0          ellipsis_0.3.1
    
+##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
+##  [4] tools_3.6.1            backports_1.1.7        utf8_1.1.4            
+##  [7] R6_2.4.1               rpart_4.1-15           Hmisc_4.2-0           
+## [10] DBI_1.0.0              lazyeval_0.2.2         colorspace_1.4-1      
+## [13] nnet_7.3-12            withr_2.2.0            tidyselect_0.2.5      
+## [16] gridExtra_2.3          bit_1.1-14             compiler_3.6.1        
+## [19] cli_2.0.2              htmlTable_1.13.1       labeling_0.3          
+## [22] scales_1.0.0           checkmate_1.9.4        readr_1.3.1           
+## [25] genefilter_1.67.1      stringr_1.4.0          digest_0.6.25         
+## [28] foreign_0.8-72         rmarkdown_1.14         XVector_0.25.0        
+## [31] base64enc_0.1-3        pkgconfig_2.0.3        htmltools_0.3.6       
+## [34] styler_1.1.1           htmlwidgets_1.3        rlang_0.4.6           
+## [37] rstudioapi_0.11        RSQLite_2.1.2          acepack_1.4.1         
+## [40] dplyr_0.8.3            RCurl_1.95-4.12        GenomeInfoDbData_1.2.1
+## [43] Formula_1.2-3          Matrix_1.2-17          Rcpp_1.0.4.6          
+## [46] munsell_0.5.0          fansi_0.4.1            lifecycle_0.2.0       
+## [49] stringi_1.4.6          yaml_2.2.1             zlibbioc_1.31.0       
+## [52] grid_3.6.1             blob_1.2.0             ggrepel_0.8.1         
+## [55] crayon_1.3.4           lattice_0.20-38        splines_3.6.1         
+## [58] annotate_1.63.0        hms_0.5.3              locfit_1.5-9.1        
+## [61] knitr_1.28             pillar_1.4.4           geneplotter_1.63.0    
+## [64] XML_3.98-1.20          glue_1.4.1             evaluate_0.14         
+## [67] latticeExtra_0.6-28    data.table_1.12.2      EnhancedVolcano_1.4.0 
+## [70] vctrs_0.3.1            gtable_0.3.0           getopt_1.20.3         
+## [73] purrr_0.3.4            rematch2_2.1.0         assertthat_0.2.1      
+## [76] xfun_0.14              xtable_1.8-4           survival_2.44-1.1     
+## [79] tibble_3.0.1           AnnotationDbi_1.47.0   memoise_1.1.0         
+## [82] cluster_2.1.0          ellipsis_0.3.1
    
 
    
 
    
 
    Blighe K., S. Rana, and M. Lewis, 2020 EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling.
    
@@ -2235,7 +2237,7 @@ 
    6 Session info
    
       theme: "bootstrap3",
       context: '.toc-content',
       hashGenerator: function (text) {
-        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
       },
       ignoreSelector: ".toc-ignore",
       scrollTo: 0
diff --git a/03-rnaseq/dimension-reduction_rnaseq_01_pca.html b/03-rnaseq/dimension-reduction_rnaseq_01_pca.html
index 3f2668c9..684038a1 100644
--- a/03-rnaseq/dimension-reduction_rnaseq_01_pca.html
+++ b/03-rnaseq/dimension-reduction_rnaseq_01_pca.html
@@ -1,10 +1,11 @@
 
 
-
+
 
 
 
 
+
 
 
 
@@ -1343,6 +1344,7 @@
 }
 img {
   max-width:100%;
+  height: auto;
 }
 .tabbed-pane {
   padding-top: 12px;
@@ -1491,7 +1493,6 @@
   border: none;
   display: inline-block;
   border-radius: 4px;
-  background-color: transparent;
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
@@ -1520,12 +1521,6 @@
 }
 }
 
-@media print {
-.toc-content {
-  /* see https://github.com/w3c/csswg-drafts/issues/4434 */
-  float: right;
-}
-}
 
 .toc-content {
   padding-left: 30px;
@@ -1619,6 +1614,7 @@
             
  • Differential Expression
    • 
             
  • Dimension Reduction - PCA
    • 
             
  • Dimension Reduction - UMAP
    • 
+            
  • Gene ID Conversion
    • 
 
           
          
@@ -1833,6 +1829,7 @@ 
    4.1 Install libraries
    
 
    ## The following objects are masked from 'package:Biobase':
 ## 
 ##     anyMissing, rowMedians
    
+
    ## Loading required package: BiocParallel
    
 
    ## 
 ## Attaching package: 'DelayedArray'
    
 
    ## The following objects are masked from 'package:matrixStats':
@@ -1945,13 +1942,13 @@ 
    4.6 Create PCA plot using DESeq2<
 
    plotPCA(dds_norm,
   intgroup = "refinebio_treatment"
 )
    
-
    
+
    
 
    In this chunk, we are going to add another variable to our plot for annotation.
    
 
    Now we’ll plot the PCA using both refinebio_treatment and refinebio_disease variables for labels since they are central to the androgen deprivation therapy (ADT) based hypothesis in the original paper (Sharma et al. 2018).
    
 
    plotPCA(dds_norm,
   intgroup = c("refinebio_treatment", "refinebio_disease") # Note that we are able to add another variable to the intgroup argument here by providing a vector of the variable names with  `c()` function
 )
    
-
    
+
    
 
    In the plot above, it is hard to distinguish the different refinebio_treatment values which contain the data on whether or not samples have been treated with ADT versus the refinebio_disease values which refer to the method by which the samples were obtained from patients (i.e. biopsy).
    
 
    Let’s use the ggplot2 package functionality to customize our plot further and make the annotation labels better distinguishable.
    
 
    First let’s use plotPCA() to receive and store the PCA values for plotting.
    
@@ -1998,59 +1995,58 @@ 
    6 Print session info
    
 
    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.
    
 
    # Print session info
 sessionInfo()
    
-
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
 ## [8] methods   base     
 ## 
 ## other attached packages:
-##  [1] magrittr_1.5                ggplot2_3.3.2              
-##  [3] DESeq2_1.28.1               SummarizedExperiment_1.18.2
-##  [5] DelayedArray_0.14.1         matrixStats_0.56.0         
-##  [7] Biobase_2.48.0              GenomicRanges_1.40.0       
-##  [9] GenomeInfoDb_1.24.2         IRanges_2.22.2             
-## [11] S4Vectors_0.26.1            BiocGenerics_0.34.0        
-## [13] optparse_1.6.6             
+##  [1] magrittr_1.5                ggplot2_3.2.1              
+##  [3] DESeq2_1.26.0               SummarizedExperiment_1.15.6
+##  [5] DelayedArray_0.11.4         BiocParallel_1.19.2        
+##  [7] matrixStats_0.54.0          Biobase_2.45.0             
+##  [9] GenomicRanges_1.37.14       GenomeInfoDb_1.21.1        
+## [11] IRanges_2.19.10             S4Vectors_0.23.23          
+## [13] BiocGenerics_0.31.5         optparse_1.6.2             
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] bit64_0.9-7.1          splines_4.0.2          R.utils_2.9.2         
-##  [4] assertthat_0.2.1       blob_1.2.1             GenomeInfoDbData_1.2.3
-##  [7] yaml_2.2.1             pillar_1.4.6           RSQLite_2.2.0         
-## [10] backports_1.1.9        lattice_0.20-41        glue_1.4.1            
-## [13] digest_0.6.25          RColorBrewer_1.1-2     XVector_0.28.0        
-## [16] colorspace_1.4-1       htmltools_0.5.0        Matrix_1.2-18         
-## [19] R.oo_1.23.0            XML_3.99-0.5           pkgconfig_2.0.3       
-## [22] genefilter_1.70.0      zlibbioc_1.34.0        purrr_0.3.4           
-## [25] xtable_1.8-4           scales_1.1.1           getopt_1.20.3         
-## [28] BiocParallel_1.22.0    tibble_3.0.3           annotate_1.66.0       
-## [31] styler_1.3.2           farver_2.0.3           generics_0.0.2        
-## [34] ellipsis_0.3.1         withr_2.2.0            cli_2.0.2             
-## [37] survival_3.1-12        crayon_1.3.4           memoise_1.1.0         
-## [40] evaluate_0.14          R.methodsS3_1.8.0      fansi_0.4.1           
-## [43] R.cache_0.14.0         tools_4.0.2            hms_0.5.3             
-## [46] lifecycle_0.2.0        stringr_1.4.0          locfit_1.5-9.4        
-## [49] munsell_0.5.0          AnnotationDbi_1.50.3   compiler_4.0.2        
-## [52] rlang_0.4.7            grid_4.0.2             RCurl_1.98-1.2        
-## [55] rstudioapi_0.11        labeling_0.3           bitops_1.0-6          
-## [58] rmarkdown_2.3          gtable_0.3.0           DBI_1.1.0             
-## [61] rematch2_2.1.2         R6_2.4.1               dplyr_1.0.0           
-## [64] knitr_1.29             bit_1.1-15.2           readr_1.3.1           
-## [67] stringi_1.4.6          Rcpp_1.0.5             vctrs_0.3.2           
-## [70] geneplotter_1.66.0     tidyselect_1.1.0       xfun_0.16
    
+##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
+##  [4] tools_3.6.1            backports_1.1.7        R6_2.4.1              
+##  [7] rpart_4.1-15           Hmisc_4.2-0            DBI_1.0.0             
+## [10] lazyeval_0.2.2         colorspace_1.4-1       nnet_7.3-12           
+## [13] withr_2.2.0            tidyselect_0.2.5       gridExtra_2.3         
+## [16] bit_1.1-14             compiler_3.6.1         cli_2.0.2             
+## [19] htmlTable_1.13.1       labeling_0.3           scales_1.0.0          
+## [22] checkmate_1.9.4        readr_1.3.1            genefilter_1.67.1     
+## [25] stringr_1.4.0          digest_0.6.25          foreign_0.8-72        
+## [28] rmarkdown_1.14         XVector_0.25.0         base64enc_0.1-3       
+## [31] pkgconfig_2.0.3        htmltools_0.3.6        styler_1.1.1          
+## [34] htmlwidgets_1.3        rlang_0.4.6            rstudioapi_0.11       
+## [37] RSQLite_2.1.2          acepack_1.4.1          dplyr_0.8.3           
+## [40] RCurl_1.95-4.12        GenomeInfoDbData_1.2.1 Formula_1.2-3         
+## [43] Matrix_1.2-17          Rcpp_1.0.4.6           munsell_0.5.0         
+## [46] fansi_0.4.1            lifecycle_0.2.0        stringi_1.4.6         
+## [49] yaml_2.2.1             zlibbioc_1.31.0        grid_3.6.1            
+## [52] blob_1.2.0             crayon_1.3.4           lattice_0.20-38       
+## [55] splines_3.6.1          annotate_1.63.0        hms_0.5.3             
+## [58] locfit_1.5-9.1         knitr_1.28             pillar_1.4.4          
+## [61] geneplotter_1.63.0     XML_3.98-1.20          glue_1.4.1            
+## [64] evaluate_0.14          latticeExtra_0.6-28    data.table_1.12.2     
+## [67] vctrs_0.3.1            gtable_0.3.0           getopt_1.20.3         
+## [70] purrr_0.3.4            rematch2_2.1.0         assertthat_0.2.1      
+## [73] xfun_0.14              xtable_1.8-4           survival_2.44-1.1     
+## [76] tibble_3.0.1           AnnotationDbi_1.47.0   memoise_1.1.0         
+## [79] cluster_2.1.0          ellipsis_0.3.1
    
 
    
 
    
 
    Freytag S., 2019 Workshop: Dimension reduction with r
    
@@ -2123,7 +2119,7 @@ 
    6 Print session info
    
       theme: "bootstrap3",
       context: '.toc-content',
       hashGenerator: function (text) {
-        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
       },
       ignoreSelector: ".toc-ignore",
       scrollTo: 0
diff --git a/03-rnaseq/dimension-reduction_rnaseq_02_umap.html b/03-rnaseq/dimension-reduction_rnaseq_02_umap.html
index 39cd9332..9648c13c 100644
--- a/03-rnaseq/dimension-reduction_rnaseq_02_umap.html
+++ b/03-rnaseq/dimension-reduction_rnaseq_02_umap.html
@@ -1,10 +1,11 @@
 
 
-
+
 
 
 
 
+
 
 
 
@@ -1343,6 +1344,7 @@
 }
 img {
   max-width:100%;
+  height: auto;
 }
 .tabbed-pane {
   padding-top: 12px;
@@ -1491,7 +1493,6 @@
   border: none;
   display: inline-block;
   border-radius: 4px;
-  background-color: transparent;
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
@@ -1520,12 +1521,6 @@
 }
 }
 
-@media print {
-.toc-content {
-  /* see https://github.com/w3c/csswg-drafts/issues/4434 */
-  float: right;
-}
-}
 
 .toc-content {
   padding-left: 30px;
@@ -1619,6 +1614,7 @@
             
  • Differential Expression
    • 
             
  • Dimension Reduction - PCA
    • 
             
  • Dimension Reduction - UMAP
    • 
+            
  • Gene ID Conversion
    • 
 
           
          
@@ -1837,6 +1833,7 @@ 
    4.1 Install libraries
    
 
    ## The following objects are masked from 'package:Biobase':
 ## 
 ##     anyMissing, rowMedians
    
+
    ## Loading required package: BiocParallel
    
 
    ## 
 ## Attaching package: 'DelayedArray'
    
 
    ## The following objects are masked from 'package:matrixStats':
@@ -1983,7 +1980,7 @@ 
    4.10 Create UMAP plot
    
   )
 ) +
   geom_point() # This tells R that we want a scatterplot
    
-
    
+
    
 
    Let’s try adding a variable to our plot for annotation.
    
 
    In this code chunk, the variable refinebio_treatment is given to the ggplot() function so we can label by androgen deprivation therapy (ADT) status.
    
 
    # Plot using `ggplot()` function
@@ -1996,7 +1993,7 @@ 
    4.10 Create UMAP plot
    
   )
 ) +
   geom_point() # This tells R that we want a scatterplot
    
-
    
+
    
 
    In the next code chunk, we are going to add another variable to our plot for annotation.
    
 
    We’ll plot using both refinebio_treatment and refinebio_disease variables for labels since they are central to the androgen deprivation therapy (ADT) based hypothesis in the original paper (Sharma et al. 2018).
    
 
    # Plot using `ggplot()` function
@@ -2013,7 +2010,7 @@ 
    4.10 Create UMAP plot
    
 
 # Display the plot that we saved above
 final_annotated_umap_plot
    
-
    
+
    
 
    Although it does appear that majority of the pre-ADT and post-ADT appear to cluster together, there are still questions remaining as we look at outliers.
    
 
    
 
    4.10.1 Interpretation of UMAP plot and results
    
@@ -2053,61 +2050,61 @@ 
    6 Print session info
    
 
    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of softwares and packages you used to run this.
    
 
    # Print session info
 sessionInfo()
    
-
    ## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04 LTS
+
    ## R version 3.6.1 (2019-07-05)
+## Platform: x86_64-apple-darwin15.6.0 (64-bit)
+## Running under: macOS Mojave 10.14.6
 ## 
 ## Matrix products: default
-## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
+## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 ## 
 ## attached base packages:
 ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
 ## [8] methods   base     
 ## 
 ## other attached packages:
-##  [1] magrittr_1.5                ggplot2_3.3.2              
-##  [3] umap_0.2.6.0                DESeq2_1.28.1              
-##  [5] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
-##  [7] matrixStats_0.56.0          Biobase_2.48.0             
-##  [9] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
-## [11] IRanges_2.22.2              S4Vectors_0.26.1           
-## [13] BiocGenerics_0.34.0         optparse_1.6.6             
+##  [1] magrittr_1.5                ggplot2_3.2.1              
+##  [3] umap_0.2.3                  DESeq2_1.26.0              
+##  [5] SummarizedExperiment_1.15.6 DelayedArray_0.11.4        
+##  [7] BiocParallel_1.19.2         matrixStats_0.54.0         
+##  [9] Biobase_2.45.0              GenomicRanges_1.37.14      
+## [11] GenomeInfoDb_1.21.1         IRanges_2.19.10            
+## [13] S4Vectors_0.23.23           BiocGenerics_0.31.5        
+## [15] optparse_1.6.2             
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] bitops_1.0-6           bit64_0.9-7.1          RColorBrewer_1.1-2    
-##  [4] R.cache_0.14.0         tools_4.0.2            backports_1.1.9       
-##  [7] R6_2.4.1               DBI_1.1.0              colorspace_1.4-1      
-## [10] withr_2.2.0            tidyselect_1.1.0       bit_1.1-15.2          
-## [13] compiler_4.0.2         cli_2.0.2              labeling_0.3          
-## [16] scales_1.1.1           readr_1.3.1            genefilter_1.70.0     
-## [19] askpass_1.1            stringr_1.4.0          digest_0.6.25         
-## [22] rmarkdown_2.3          R.utils_2.9.2          XVector_0.28.0        
-## [25] pkgconfig_2.0.3        htmltools_0.5.0        styler_1.3.2          
-## [28] rlang_0.4.7            rstudioapi_0.11        RSQLite_2.2.0         
-## [31] farver_2.0.3           generics_0.0.2         jsonlite_1.7.0        
-## [34] BiocParallel_1.22.0    dplyr_1.0.0            R.oo_1.23.0           
-## [37] RCurl_1.98-1.2         GenomeInfoDbData_1.2.3 Matrix_1.2-18         
-## [40] Rcpp_1.0.5             munsell_0.5.0          fansi_0.4.1           
-## [43] reticulate_1.16        lifecycle_0.2.0        R.methodsS3_1.8.0     
-## [46] stringi_1.4.6          yaml_2.2.1             zlibbioc_1.34.0       
-## [49] grid_4.0.2             blob_1.2.1             crayon_1.3.4          
-## [52] lattice_0.20-41        splines_4.0.2          annotate_1.66.0       
-## [55] hms_0.5.3              locfit_1.5-9.4         knitr_1.29            
-## [58] pillar_1.4.6           geneplotter_1.66.0     XML_3.99-0.5          
-## [61] glue_1.4.1             evaluate_0.14          vctrs_0.3.2           
-## [64] gtable_0.3.0           openssl_1.4.2          getopt_1.20.3         
-## [67] purrr_0.3.4            rematch2_2.1.2         assertthat_0.2.1      
-## [70] xfun_0.16              xtable_1.8-4           RSpectra_0.16-0       
-## [73] survival_3.1-12        tibble_3.0.3           AnnotationDbi_1.50.3  
-## [76] memoise_1.1.0          ellipsis_0.3.1
    
+##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
+##  [4] tools_3.6.1            backports_1.1.7        R6_2.4.1              
+##  [7] rpart_4.1-15           Hmisc_4.2-0            DBI_1.0.0             
+## [10] lazyeval_0.2.2         colorspace_1.4-1       nnet_7.3-12           
+## [13] withr_2.2.0            tidyselect_0.2.5       gridExtra_2.3         
+## [16] bit_1.1-14             compiler_3.6.1         cli_2.0.2             
+## [19] htmlTable_1.13.1       labeling_0.3           scales_1.0.0          
+## [22] checkmate_1.9.4        readr_1.3.1            genefilter_1.67.1     
+## [25] askpass_1.1            stringr_1.4.0          digest_0.6.25         
+## [28] foreign_0.8-72         rmarkdown_1.14         XVector_0.25.0        
+## [31] base64enc_0.1-3        pkgconfig_2.0.3        htmltools_0.3.6       
+## [34] styler_1.1.1           htmlwidgets_1.3        rlang_0.4.6           
+## [37] rstudioapi_0.11        RSQLite_2.1.2          jsonlite_1.7.0        
+## [40] acepack_1.4.1          dplyr_0.8.3            RCurl_1.95-4.12       
+## [43] GenomeInfoDbData_1.2.1 Formula_1.2-3          Matrix_1.2-17         
+## [46] Rcpp_1.0.4.6           munsell_0.5.0          fansi_0.4.1           
+## [49] reticulate_1.13        lifecycle_0.2.0        stringi_1.4.6         
+## [52] yaml_2.2.1             zlibbioc_1.31.0        grid_3.6.1            
+## [55] blob_1.2.0             crayon_1.3.4           lattice_0.20-38       
+## [58] splines_3.6.1          annotate_1.63.0        hms_0.5.3             
+## [61] locfit_1.5-9.1         knitr_1.28             pillar_1.4.4          
+## [64] geneplotter_1.63.0     XML_3.98-1.20          glue_1.4.1            
+## [67] evaluate_0.14          latticeExtra_0.6-28    data.table_1.12.2     
+## [70] vctrs_0.3.1            openssl_1.4.1          gtable_0.3.0          
+## [73] getopt_1.20.3          purrr_0.3.4            rematch2_2.1.0        
+## [76] assertthat_0.2.1       xfun_0.14              xtable_1.8-4          
+## [79] RSpectra_0.15-0        survival_2.44-1.1      tibble_3.0.1          
+## [82] AnnotationDbi_1.47.0   memoise_1.1.0          cluster_2.1.0         
+## [85] ellipsis_0.3.1
    
 
    
 
    
 
    CCDL, 2020 ScRNA-seq dimension reduction.
    
@@ -2189,7 +2186,7 @@ 
    6 Print session info
    
       theme: "bootstrap3",
       context: '.toc-content',
       hashGenerator: function (text) {
-        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
       },
       ignoreSelector: ".toc-ignore",
       scrollTo: 0
diff --git a/04-advanced-topics/00-intro-to-advanced-topics.html b/04-advanced-topics/00-intro-to-advanced-topics.html
index f22e3487..62cbcc64 100644
--- a/04-advanced-topics/00-intro-to-advanced-topics.html
+++ b/04-advanced-topics/00-intro-to-advanced-topics.html
@@ -1,10 +1,11 @@
 
 
-
+
 
 
 
 
+
 
 
 
@@ -1343,6 +1344,7 @@
 }
 img {
   max-width:100%;
+  height: auto;
 }
 .tabbed-pane {
   padding-top: 12px;
@@ -1491,7 +1493,6 @@
   border: none;
   display: inline-block;
   border-radius: 4px;
-  background-color: transparent;
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
@@ -1520,12 +1521,6 @@
 }
 }
 
-@media print {
-.toc-content {
-  /* see https://github.com/w3c/csswg-drafts/issues/4434 */
-  float: right;
-}
-}
 
 .toc-content {
   padding-left: 30px;
@@ -1619,6 +1614,7 @@
             
  • Differential Expression
    • 
             
  • Dimension Reduction - PCA
    • 
             
  • Dimension Reduction - UMAP
    • 
+            
  • Gene ID Conversion
    • 
 
           
          
@@ -1719,7 +1715,7 @@ 
    CCDL for ALSF
    
       theme: "bootstrap3",
       context: '.toc-content',
       hashGenerator: function (text) {
-        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
       },
       ignoreSelector: ".toc-ignore",
       scrollTo: 0
diff --git a/Snakefile b/Snakefile
index afbd64a2..3c17484e 100644
--- a/Snakefile
+++ b/Snakefile
@@ -6,6 +6,7 @@ rule target:
         "02-microarray/clustering_microarray_01_heatmap.html",
         "02-microarray/dimension-reduction_microarray_01_pca.html",
         "02-microarray/dimension-reduction_microarray_02_umap.html",
+        "02-microarray/gene-id-convert_microarray_01_ensembl.html",
         "03-rnaseq/clustering_rnaseq_01_heatmap.html",
         "03-rnaseq/differential-expression_rnaseq_01.html",
         "03-rnaseq/00-intro-to-rnaseq.html",
diff --git a/components/_navbar.html b/components/_navbar.html
index 55bc3fde..12032545 100644
--- a/components/_navbar.html
+++ b/components/_navbar.html
@@ -26,6 +26,7 @@
             
  • Differential Expression
    • 
             
  • Dimension Reduction - PCA
    • 
             
  • Dimension Reduction - UMAP
    • 
+            
  • Gene ID Conversion
    • 
 
           
          
diff --git a/references.bib b/references.bib
index 35439d5d..94366990 100644
--- a/references.bib
+++ b/references.bib
@@ -14,6 +14,12 @@ @Article{Brems2017
   url = {https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c},
 }
 
+@Website{Carlson2020,
+  title = {Genome wide annotation for Mouse},
+  author = {Marc Carlson}, 
+  url = {https://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html},
+}
+
 @Manual{CCDL2020,
   title = {scRNA-seq Dimension Reduction},
   author = {CCDL},

From 5a1f27b6de091f3930a5fe39d497953821d0d7c4 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Thu, 10 Sep 2020 15:42:41 -0400
Subject: [PATCH 02/18] add section to explore the mapped df and add some
 context

---
 .../gene-id-convert_microarray_01_ensembl.Rmd |  55 +++++---
 ...gene-id-convert_microarray_01_ensembl.html | 119 ++++++++++++++++--
 2 files changed, 150 insertions(+), 24 deletions(-)

diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
index 210f0a4b..12ea9924 100644
--- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
+++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
@@ -65,7 +65,7 @@ In the same place you put this `.Rmd` file, you should now have three new empty
 
 For general information about downloading data for these examples, see our ['Getting Started' section](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-the-data). 
 
-Go to this [dataset's page on refine.bio]({{REFINEBIO DATASET PAGE LINK}}).
+Go to this [dataset's page on refine.bio](https://www.refine.bio/experiments/GSE13490/cancer-stem-cells-are-enriched-in-the-side-population-cells-in-a-mouse-model-of-glioma).
 
 Click the "Download Now" button on the right side of this screen.  
 
@@ -75,15 +75,6 @@ Fill out the pop up window with your email and our Terms and Conditions:
 
   
 
-
-{{DELETE THIS LINE}}
-We are going to use non-quantile normalized data for this analysis.
-To get this data, you will need to check the box that says "Skip quantile normalization for RNA-seq samples".
-Note that this option will only be available for RNA-seq datasets.
-
-
-{{DELETE THIS LINE}}
-
 It may take a few minutes for the dataset to process.
 You will get an email when it is ready. 
 
@@ -91,7 +82,8 @@ You will get an email when it is ready.
 
 For this example analysis, we will use this [mouse glioma stem cells dataset](https://www.refine.bio/experiments/GSE13490/cancer-stem-cells-are-enriched-in-the-side-population-cells-in-a-mouse-model-of-glioma).
 
- 
+The data that we downloaded from refine.bio for this analysis has 15 microarray mouse glioma model samples.
+The samples were obtained from parental biological replicates and from resistant sub-line biological replicates that were transplanted into recipient mice.
 
 ## Place the dataset in your new `data/` folder
 
@@ -153,16 +145,16 @@ From here you can customize this analysis example to fit your own scientific que
   
 
 
-# {{NAME OF example}}
+# Obtaining Annotation for Ensembl IDs - Microarray
 
 Ensembl IDs can be used to obtain various different annotations at the 
 gene/transcript level.
-Although this example script uses Ensembl IDs from Zebrafish, (Danio rerio),
+Although this example script uses Ensembl IDs from Mouse, (Mus musculus),
 to obtain gene symbols this script can be easily converted for use with different 
 species or annotation types eg protein IDs, gene ontology, accession 
 numbers. 
 
-For different species, wherever the abbreviation `org.Dr.eg.db` or `Dr` is 
+For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is 
 written, it must be replaced with the respective species abbreviation eg
 for Homo sapiens `org.Hs.eg.db` or `Hs` would be used.
 A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf).
@@ -246,6 +238,41 @@ mapped_df <-
   )
 ```
 
+## Explore mapped data frame
+
+```{r}
+# First, let's use the `head()` function to take a preview at our mapped data frame
+head(mapped_df)
+```
+
+Now let's get a summary of the symbols returned in our mapped data frame.
+
+```{r}
+summary(mapped_df$Symbols)
+```
+
+There are 998 NA's in our data frame, which means that 998 of the Ensembl IDs did not map to gene symbols.
+
+Now let's check to see if we have any genes that were mapped to multiple symbols.
+
+```{r}
+multi_mapped <- mapped_df %>%
+  # Let's isolate our `Symbols` and `Gene` columns
+  dplyr::select(Symbols, Gene) %>%
+  # Now we are going to group by the Ensembl ID's in the `Gene` column
+  dplyr::group_by(Gene) %>%
+  # Create a new variable containing the number of symbols mapped to each ID
+  dplyr::mutate(gene_symbol_count = n()) %>%
+  # Arrange by the genes with the highest number of symbols mapped
+  dplyr::arrange(gene_symbol_count)
+
+# Let's look at the first 6 rows of our `multi_mapped` object
+head(multi_mapped)
+```
+
+Looks like we do not have any cases of genes with multiple mappings in this particular dataset.
+This is not the case for every dataset, this step is good practice when mapping to symbols use gene identifiers.
+
 ## Write to file
 
 ```{r Write to file}
diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html
index 5fe90296..952451b3 100644
--- a/02-microarray/gene-id-convert_microarray_01_ensembl.html
+++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html
@@ -1706,19 +1706,17 @@ 
    2.2 Set up your analysis folders<
 
    
 
    2.3 Obtain the dataset from refine.bio
    
 
    For general information about downloading data for these examples, see our ‘Getting Started’ section.
    
-
    Go to this dataset’s page on refine.bio.
    
+
    Go to this dataset’s page on refine.bio.
    
 
    Click the “Download Now” button on the right side of this screen.
    
 
    
 
    Fill out the pop up window with your email and our Terms and Conditions:
    
 
    
-
    {{DELETE THIS LINE}} We are going to use non-quantile normalized data for this analysis. To get this data, you will need to check the box that says “Skip quantile normalization for RNA-seq samples”. Note that this option will only be available for RNA-seq datasets.
    
-
     {{DELETE THIS LINE}}
    
 
    It may take a few minutes for the dataset to process. You will get an email when it is ready.
    
 
    
 
    
 
    2.4 About the dataset we are using for this example
    
 
    For this example analysis, we will use this mouse glioma stem cells dataset.
    
-
+
    The data that we downloaded from refine.bio for this analysis has 15 microarray mouse glioma model samples. The samples were obtained from parental biological replicates and from resistant sub-line biological replicates that were transplanted into recipient mice.
    
 
    
 
    
 
    2.5 Place the dataset in your new data/ folder
    
@@ -1771,10 +1769,10 @@ 
    3 Using a different refine.bio da
 
 
     
    
 
    
-
    
-
    4 {{NAME OF example}}
    
-
    Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Although this example script uses Ensembl IDs from Zebrafish, (Danio rerio), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.
    
-
    For different species, wherever the abbreviation org.Dr.eg.db or Dr is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link.
    
+
    
+
    4 Obtaining Annotation for Ensembl IDs - Microarray
    
+
    Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.
    
+
    For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link.
    
 
    
 
    4.1 Install libraries
    
 
    See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.
    
@@ -1910,8 +1908,109 @@ 
    4.3 Map Ensembl IDs to gene symbo
   )
    
 
    ## 'select()' returned 1:many mapping between keys and columns
    
 
    
+
    
+
    4.4 Explore mapped data frame
    
+
    # First, let's use the `head()` function to take a preview at our mapped data frame
+head(mapped_df)
    
+
    ##                    Symbols               Gene GSM340068   GSM340072   GSM340071
+## ENSMUSG00000000001   Gnai3 ENSMUSG00000000001 9.0936148  7.99950284  8.95697104
+## ENSMUSG00000000003    Pbsn ENSMUSG00000000003 0.1158669 -0.01806812  0.33961890
+## ENSMUSG00000000028   Cdc45 ENSMUSG00000000028 4.0955659  4.08404318  6.40406029
+## ENSMUSG00000000031     H19 ENSMUSG00000000031 3.6377112  1.99403634  2.20337978
+## ENSMUSG00000000037   Scml2 ENSMUSG00000000037 0.3255582  2.50103467  0.87962948
+## ENSMUSG00000000049    Apoh ENSMUSG00000000049 0.9238867  0.90178719 -0.07378849
+##                     GSM340075 GSM340073 GSM340078 GSM340076  GSM340069
+## ENSMUSG00000000001 9.57407861 7.4204583 8.0817096 9.0049117 10.0229721
+## ENSMUSG00000000003 0.05642225 0.1751004 0.1402899 0.1945611  0.1729683
+## ENSMUSG00000000028 5.39607105 4.4854625 4.3000140 2.6582618  5.0904650
+## ENSMUSG00000000031 1.11520094 1.1794732 3.1565971 1.0680197 10.1382983
+## ENSMUSG00000000037 0.93308509 2.1829868 2.2657694 1.2691216  0.4042593
+## ENSMUSG00000000049 0.08114104 0.6793026 0.9798333 0.9241858  0.8410028
+##                    GSM340065    GSM340070  GSM340067   GSM340074 GSM340066
+## ENSMUSG00000000001 7.8532377  9.214928841 5.54997452 9.654812590 8.2018749
+## ENSMUSG00000000003 0.2580271 -0.001017382 0.01480985 0.546946872 0.2348428
+## ENSMUSG00000000028 4.7374842  5.148726742 2.69213997 4.780000261 3.0079289
+## ENSMUSG00000000031 1.9280822  3.169991848 0.19701060 0.954261673 0.7830665
+## ENSMUSG00000000037 2.1511658  0.762200101 0.82645739 0.781790693 1.3032066
+## ENSMUSG00000000049 0.3368805  0.107442284 1.63785952 0.003141663 0.5640828
+##                    GSM340064  GSM340077
+## ENSMUSG00000000001 7.8776291 6.78553772
+## ENSMUSG00000000003 0.2198192 0.04734498
+## ENSMUSG00000000028 5.5948725 6.10847476
+## ENSMUSG00000000031 3.2268280 0.80851073
+## ENSMUSG00000000037 1.4112563 1.74052445
+## ENSMUSG00000000049 0.5473140 0.08018819
    
+
    Now let’s get a summary of the symbols returned in our mapped data frame.
    
+
    summary(mapped_df$Symbols)
    
+
    ##          Pms2 0610005C13Rik 0610009B22Rik 0610009L18Rik 0610010F05Rik 
+##             2             1             1             1             1 
+## 0610012G03Rik 0610030E20Rik 0610038B21Rik 0610039K10Rik 0610040B10Rik 
+##             1             1             1             1             1 
+## 0610040F04Rik 0610040J01Rik 1110002E22Rik 1110002L01Rik 1110004F10Rik 
+##             1             1             1             1             1 
+## 1110006O24Rik 1110008L16Rik 1110008P14Rik 1110015O18Rik 1110017D15Rik 
+##             1             1             1             1             1 
+## 1110019D14Rik 1110020A21Rik 1110028F18Rik 1110032A03Rik 1110032F04Rik 
+##             1             1             1             1             1 
+## 1110038F14Rik 1110046J04Rik 1110051M20Rik 1110059E24Rik 1110059G10Rik 
+##             1             1             1             1             1 
+## 1110065P20Rik 1190005I06Rik 1300002E11Rik 1300017J02Rik 1500009C09Rik 
+##             1             1             1             1             1 
+## 1500009L16Rik 1500011B03Rik 1500015A07Rik 1500015O10Rik 1600002K03Rik 
+##             1             1             1             1             1 
+## 1600010M07Rik 1600012H06Rik 1600014C10Rik 1600014C23Rik 1600015I10Rik 
+##             1             1             1             1             1 
+## 1600020E01Rik 1600023N17Rik 1600025M17Rik 1600029I14Rik 1600029O15Rik 
+##             1             1             1             1             1 
+## 1700001C02Rik 1700001C19Rik 1700001G11Rik 1700001G17Rik 1700001K19Rik 
+##             1             1             1             1             1 
+## 1700001L19Rik 1700001O22Rik 1700001P01Rik 1700003D09Rik 1700003E16Rik 
+##             1             1             1             1             1 
+## 1700003F12Rik 1700003G18Rik 1700003H04Rik 1700003P14Rik 1700006A11Rik 
+##             1             1             1             1             1 
+## 1700007F19Rik 1700007J10Rik 1700007K13Rik 1700007L15Rik 1700008J07Rik 
+##             1             1             1             1             1 
+## 1700008K24Rik 1700008O03Rik 1700008P02Rik 1700009J07Rik 1700009N14Rik 
+##             1             1             1             1             1 
+## 1700010B08Rik 1700010I14Rik 1700011B04Rik 1700011L22Rik 1700011M02Rik 
+##             1             1             1             1             1 
+## 1700012A03Rik 1700012B07Rik 1700012B09Rik 1700012D01Rik 1700012D14Rik 
+##             1             1             1             1             1 
+## 1700012I11Rik 1700012P22Rik 1700013F07Rik 1700013G24Rik 1700013H16Rik 
+##             1             1             1             1             1 
+## 1700015F17Rik 1700015G11Rik 1700016A09Rik 1700016C15Rik 1700016D06Rik 
+##             1             1             1             1             1 
+## 1700016H13Rik 1700016K19Rik 1700016P04Rik       (Other)          NA's 
+##             1             1             1         16821           998
    
+
    There are 998 NA’s in our data frame, which means that 998 of the Ensembl IDs did not map to gene symbols.
    
+
    Now let’s check to see if we have any genes that were mapped to multiple symbols.
    
+
    multi_mapped <- mapped_df %>%
+  # Let's isolate our `Symbols` and `Gene` columns
+  dplyr::select(Symbols, Gene) %>%
+  # Now we are going to group by the Ensembl ID's in the `Gene` column
+  dplyr::group_by(Gene) %>%
+  # Create a new variable containing the number of symbols mapped to each ID
+  dplyr::mutate(gene_symbol_count = n()) %>%
+  # Arrange by the genes with the highest number of symbols mapped
+  dplyr::arrange(gene_symbol_count)
    
+
    ## Warning: Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()`.
+## This warning is displayed once per session.
    
+
    # Let's look at the first 6 rows of our `multi_mapped` object
+head(multi_mapped)
    
+
    ## # A tibble: 6 x 3
+## # Groups:   Gene [6]
+##   Symbols Gene               gene_symbol_count
+##   <fct>   <chr>                          <int>
+## 1 Gnai3   ENSMUSG00000000001                 1
+## 2 Pbsn    ENSMUSG00000000003                 1
+## 3 Cdc45   ENSMUSG00000000028                 1
+## 4 H19     ENSMUSG00000000031                 1
+## 5 Scml2   ENSMUSG00000000037                 1
+## 6 Apoh    ENSMUSG00000000049                 1
    
+
    Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping to symbols use gene identifiers.
    
+
    
 
    
-
    4.4 Write to file
    
+
    4.5 Write to file
    
 
    # Write mapped and annotated data frame to output file
 readr::write_tsv(mapped_df, file.path(
   results_dir,
@@ -1961,7 +2060,7 @@ 
    6 Session info
    
 ## [29] glue_1.4.1       R6_2.4.1         fansi_0.4.1      rmarkdown_1.14  
 ## [33] blob_1.2.0       readr_1.3.1      purrr_0.3.4      rematch2_2.1.0  
 ## [37] backports_1.1.7  ellipsis_0.3.1   htmltools_0.3.6  assertthat_0.2.1
-## [41] stringi_1.4.6    crayon_1.3.4
    
+## [41] utf8_1.1.4       stringi_1.4.6    crayon_1.3.4

    Carlson M., Genome wide annotation for mouse

    From d39c19328b98e22b92e5b28ee103661228ee963a Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 11 Sep 2020 11:05:33 -0400 Subject: [PATCH 03/18] add context around `multiVals` - add references to `references.bib` - update comments and documentation - rerun Snakefile --- .../gene-id-convert_microarray_01_ensembl.Rmd | 32 +++++++++++------ ...gene-id-convert_microarray_01_ensembl.html | 34 ++++++++++++------- references.bib | 19 +++++++++-- 3 files changed, 59 insertions(+), 26 deletions(-) diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd index 12ea9924..dcf979be 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd @@ -104,7 +104,7 @@ Copy and paste the `GSE13490` folder into your newly created `data/` folder. Your new analysis folder should contain: -- The example analysis Rmd you downloaded +- The example analysis `.Rmd` you downloaded - A folder called "data" which contains: - The `GSE13490` folder which contains: - The gene expression @@ -157,13 +157,13 @@ numbers. For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. -A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf). +A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData) [@annotation-packages]. ## Install libraries See our Getting Started page with [instructions for package installation](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#what-you-need-to-install) for a list of the other software you will need, as well as more tips and resources. -In this analysis, we will be using the `org.Mm.eg.db` R package [@Carlson2020]. +In this analysis, we will be using the `org.Mm.eg.db` R package [@Carlson2019]. ```{r} # Install the mouse package @@ -222,17 +222,21 @@ df <- df %>% ## Map Ensembl IDs to gene symbols -This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect since genes and transcripts do not have a 1:1 relationship. +This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. +The `mapIDs()` function has a `multiVals` argument which denotes what to do when there are multiple mapped values for a single gene identifier. +The default behavior is to return just the first mapped value. +Use `?mapIds` to see more options. + ```{r} # Map ensembl IDs to their associated gene symbols mapped_df <- data.frame( "Symbols" = mapIds( - org.Mm.eg.db, + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data keys = df$Gene, column = "SYMBOL", - keytype = "ENSEMBL" + keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data ), df ) @@ -240,18 +244,24 @@ mapped_df <- ## Explore mapped data frame +Now, let's take a look at our mapped data. + ```{r} -# First, let's use the `head()` function to take a preview at our mapped data frame +# Let's use the `head()` function to take a preview at our mapped data frame head(mapped_df) ``` -Now let's get a summary of the symbols returned in our mapped data frame. +We can see that our data frame has a new column `Symbols`. +Let's get a summary of the values returned in the `Symbols` column of our mapped data frame. ```{r} +# We can use the `summary()` function to get a better idea of the distribution of values in the `Symbols` column summary(mapped_df$Symbols) ``` -There are 998 NA's in our data frame, which means that 998 of the Ensembl IDs did not map to gene symbols. +There are 998 NA's in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. +If nothing is mapping to your gene identifier, it is possible that the function was executed incorrectly or a different gene identifier should be used. +In some cases, this behavior is expected, but it is always good to be aware of how many genes you are potentially "losing" if you rely on this new gene identifier you've mapped to for downstream analyses. Now let's check to see if we have any genes that were mapped to multiple symbols. @@ -271,7 +281,7 @@ head(multi_mapped) ``` Looks like we do not have any cases of genes with multiple mappings in this particular dataset. -This is not the case for every dataset, this step is good practice when mapping to symbols use gene identifiers. +This is not the case for every dataset, this step is good practice when mapping between gene identifiers. ## Write to file @@ -286,7 +296,7 @@ readr::write_tsv(mapped_df, file.path( # Resources for further learning -- [{{HELPFUL RESOURCE NAME}}]({{LINK TO HELPFUL RESOURCE}}) [{{CITATION}}] +- Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020] # Session info diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html index 952451b3..991272c9 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.html +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html @@ -1730,7 +1730,7 @@

    2.5 Place the dataset in your new

    2.6 Check out our file structure!

    Your new analysis folder should contain:

      -
    • The example analysis Rmd you downloaded
      +
    • The example analysis .Rmd you downloaded
    • A folder called “data” which contains:
        @@ -1772,11 +1772,11 @@

        3 Using a different refine.bio da

        4 Obtaining Annotation for Ensembl IDs - Microarray

        Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

        -

        For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link.

        +

        For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).

        4.1 Install libraries

        See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.

        -

        In this analysis, we will be using the org.Mm.eg.db R package (Carlson).

        +

        In this analysis, we will be using the org.Mm.eg.db R package (Carlson 2019).

        # Install the mouse package
 if (!("org.Mm.eg.db" %in% installed.packages())) {
   # Install this package if it isn't installed yet
@@ -1894,15 +1894,15 @@ 
        4.2 Import and set up data

        4.3 Map Ensembl IDs to gene symbols

        -

        This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect since genes and transcripts do not have a 1:1 relationship.

        +

        This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIDs() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. Use ?mapIds to see more options.

        # Map ensembl IDs to their associated gene symbols
 mapped_df <-
   data.frame(
     "Symbols" = mapIds(
-      org.Mm.eg.db,
+      org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
       keys = df$Gene,
       column = "SYMBOL",
-      keytype = "ENSEMBL"
+      keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data
     ),
     df
   )
        @@ -1910,7 +1910,8 @@

        4.3 Map Ensembl IDs to gene symbo

        4.4 Explore mapped data frame

        -
        # First, let's use the `head()` function to take a preview at our mapped data frame
+
        Now, let’s take a look at our mapped data.
        
+
        # Let's use the `head()` function to take a preview at our mapped data frame
 head(mapped_df)
        
 
        ##                    Symbols               Gene GSM340068   GSM340072   GSM340071
 ## ENSMUSG00000000001   Gnai3 ENSMUSG00000000001 9.0936148  7.99950284  8.95697104
@@ -1940,8 +1941,9 @@ 
        4.4 Explore mapped data frame
        
-
        Now let’s get a summary of the symbols returned in our mapped data frame.
        
-
        summary(mapped_df$Symbols)
        
+
        We can see that our data frame has a new column Symbols. Let’s get a summary of the values returned in the Symbols column of our mapped data frame.
        
+
        # We can use the `summary()` function to get a better idea of the distribution of values in the `Symbols` column
+summary(mapped_df$Symbols)
        
 
        ##          Pms2 0610005C13Rik 0610009B22Rik 0610009L18Rik 0610010F05Rik 
 ##             2             1             1             1             1 
 ## 0610012G03Rik 0610030E20Rik 0610038B21Rik 0610039K10Rik 0610040B10Rik 
@@ -1982,7 +1984,7 @@ 
        4.4 Explore mapped data frame
        
-
        There are 998 NA’s in our data frame, which means that 998 of the Ensembl IDs did not map to gene symbols.
        
+
        There are 998 NA’s in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. If nothing is mapping to your gene identifier, it is possible that the function was executed incorrectly or a different gene identifier should be used. In some cases, this behavior is expected, but it is always good to be aware of how many genes you are potentially “losing” if you rely on this new gene identifier you’ve mapped to for downstream analyses.
        
 
        Now let’s check to see if we have any genes that were mapped to multiple symbols.
        
 
        multi_mapped <- mapped_df %>%
   # Let's isolate our `Symbols` and `Gene` columns
@@ -2007,7 +2009,7 @@ 
        4.4 Explore mapped data frame
        
-
        Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping to symbols use gene identifiers.
        
+
        Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping between gene identifiers.

        4.5 Write to file

        @@ -2021,7 +2023,7 @@

        4.5 Write to file

        5 Resources for further learning

        @@ -2062,8 +2064,14 @@

        6 Session info

        ## [37] backports_1.1.7 ellipsis_0.3.1 htmltools_0.3.6 assertthat_0.2.1 ## [41] utf8_1.1.4 stringi_1.4.6 crayon_1.3.4
        +
        +

        Carlson M., 2019 Genome wide annotation for mouse

        +
        -

        Carlson M., Genome wide annotation for mouse

        +

        Carlson M., 2020 AnnotationDbi: Introduction to bioconductor annotation packages

        +
        +
        +

        R Bioconductor Team, 2003 Packages found under annotationdata

        diff --git a/references.bib b/references.bib index 94366990..37f8df1a 100644 --- a/references.bib +++ b/references.bib @@ -1,3 +1,10 @@ +@Website{annotation-packages, + title = {Packages found under AnnotationData}, + author = {{R Bioconductor Team}}, + year = {2003}, + url = {https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData}, +} + @Manual{Blighe2020, title = {EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling}, @@ -14,12 +21,20 @@ @Article{Brems2017 url = {https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c}, } -@Website{Carlson2020, +@Website{Carlson2019, title = {Genome wide annotation for Mouse}, - author = {Marc Carlson}, + author = {Marc Carlson}, + year = {2019}, url = {https://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html}, } +@Website{Carlson2020, + title = {AnnotationDbi: Introduction To Bioconductor Annotation Packages}, + author = {Marc Carlson}, + year = {2020}, + url = {https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf}, +} + @Manual{CCDL2020, title = {scRNA-seq Dimension Reduction}, author = {CCDL}, From 2b3e3246c9d87f645100120fa799e4563b8be8a4 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 11 Sep 2020 11:25:30 -0400 Subject: [PATCH 04/18] add annotation package to dockerfile --- Dockerfile | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index b174de1f..ef393f81 100644 --- a/Dockerfile +++ b/Dockerfile @@ -51,6 +51,7 @@ RUN R -e "BiocManager::install(c('affy', 'apeglm', 'Biobase', 'ComplexHeatmap', # Installs packages needed for plottings # treemap, interactive plots, and hex plots # Rtsne and umap are required for dimension reduction analyses +# org.Mm.eg.db is required for gene mapping RUN install2.r --error --deps TRUE \ ggfortify \ ggsignif \ @@ -58,7 +59,8 @@ RUN install2.r --error --deps TRUE \ pheatmap \ Rtsne \ umap \ - VennDiagram + VennDiagram \ + org.Mm.eg.db ########################## # Install packages from github From 58bd38c5d6ba36235bd7c0c9de868dbac9222ba1 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 11 Sep 2020 11:36:37 -0400 Subject: [PATCH 05/18] add new reference - rerun Snakefile --- 02-microarray/gene-id-convert_microarray_01_ensembl.Rmd | 3 ++- 02-microarray/gene-id-convert_microarray_01_ensembl.html | 8 ++++++-- references.bib | 7 +++++++ 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd index dcf979be..fbffe772 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd @@ -283,7 +283,7 @@ head(multi_mapped) Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping between gene identifiers. -## Write to file +## Write mapped results to file ```{r Write to file} # Write mapped and annotated data frame to output file @@ -297,6 +297,7 @@ readr::write_tsv(mapped_df, file.path( # Resources for further learning - Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020] +- [More on Annotation mapping functions](https://www.bioconductor.org/packages/release/bioc/vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf) [@Edwards2020] # Session info diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html index 991272c9..35fbee1c 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.html +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html @@ -2011,8 +2011,8 @@

        4.4 Explore mapped data frame

        Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping between gene identifiers.

        -
        -

        4.5 Write to file

        +
        +

        4.5 Write mapped results to file

        # Write mapped and annotated data frame to output file
 readr::write_tsv(mapped_df, file.path(
   results_dir,
@@ -2024,6 +2024,7 @@ 
        4.5 Write to file
        
 
        5 Resources for further learning
        @@ -2070,6 +2071,9 @@

        6 Session info

        Carlson M., 2020 AnnotationDbi: Introduction to bioconductor annotation packages

        +
        +

        Edwards S. M., 2020 Annotation mapping functions.

        +

        R Bioconductor Team, 2003 Packages found under annotationdata

        diff --git a/references.bib b/references.bib index 37f8df1a..507b72fa 100644 --- a/references.bib +++ b/references.bib @@ -49,6 +49,13 @@ @Manual{CCDL2020-cluster-validation url = {https://github.com/AlexsLemonade/training-modules/blob/3dbc6f3f53c680ec6aa2f513851c1cd4635cc31c/machine-learning/02-openpbta_consensus_clustering.Rmd#L310}, } +@Manual{Edwards2020, + title = {Annotation mapping functions}, + author = {Stefan McKinnon Edwards}, + year = {2020}, + url = {https://www.bioconductor.org/packages/release/bioc/vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf}, +} + @Website{Freytag2019, title = {Workshop: Dimension reduction with R}, author = {Saskia Freytag}, From 62095e0c7e86fc3f058687d4797fcd2175ff6092 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 11 Sep 2020 11:40:54 -0400 Subject: [PATCH 06/18] fix a sentence --- 02-microarray/gene-id-convert_microarray_01_ensembl.Rmd | 2 +- 02-microarray/gene-id-convert_microarray_01_ensembl.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd index fbffe772..1a81aa5c 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd @@ -281,7 +281,7 @@ head(multi_mapped) ``` Looks like we do not have any cases of genes with multiple mappings in this particular dataset. -This is not the case for every dataset, this step is good practice when mapping between gene identifiers. +This is not the case for every dataset, therefore this step is good practice when mapping between gene identifiers. ## Write mapped results to file diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html index 35fbee1c..ec9993e3 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.html +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html @@ -2009,7 +2009,7 @@

        4.4 Explore mapped data frame

        -

        Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, this step is good practice when mapping between gene identifiers.

        +

        Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, therefore this step is good practice when mapping between gene identifiers.

        4.5 Write mapped results to file

        From a4a8b38725799fdef94c740076aa436c4f9dfdb1 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 11 Sep 2020 11:44:38 -0400 Subject: [PATCH 07/18] add more context around `multiVals` --- 02-microarray/gene-id-convert_microarray_01_ensembl.Rmd | 5 +++-- 02-microarray/gene-id-convert_microarray_01_ensembl.html | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd index 1a81aa5c..18e82fd1 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd @@ -225,7 +225,8 @@ df <- df %>% This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The `mapIDs()` function has a `multiVals` argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. -Use `?mapIds` to see more options. +We will use the default behavior in the next chunk, but it is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. +Use `?mapIds` to see more options or strategies. ```{r} @@ -244,7 +245,7 @@ mapped_df <- ## Explore mapped data frame -Now, let's take a look at our mapped data. +Now, let's take a look at our mapped data frame to see how the mapping went. ```{r} # Let's use the `head()` function to take a preview at our mapped data frame diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html index ec9993e3..80fcd693 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.html +++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html @@ -1894,7 +1894,7 @@

        4.2 Import and set up data

        4.3 Map Ensembl IDs to gene symbols

        -

        This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIDs() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. Use ?mapIds to see more options.

        +

        This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIDs() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. We will use the default behavior in the next chunk, but it is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use ?mapIds to see more options or strategies.

        # Map ensembl IDs to their associated gene symbols
 mapped_df <-
   data.frame(
@@ -1910,7 +1910,7 @@ 
        4.3 Map Ensembl IDs to gene symbo

        4.4 Explore mapped data frame

        -

        Now, let’s take a look at our mapped data.

        +

        Now, let’s take a look at our mapped data frame to see how the mapping went.

        # Let's use the `head()` function to take a preview at our mapped data frame
 head(mapped_df)
        ##                    Symbols               Gene GSM340068   GSM340072   GSM340071

From 1c66b52303745f0d0cd0155a4da03d362033cd85 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 11 Sep 2020 22:22:32 -0400
Subject: [PATCH 08/18] add a bit more context around the gene ids used

- fix formatting
---
 .../gene-id-convert_microarray_01_ensembl.Rmd    | 16 +++++-----------
 .../gene-id-convert_microarray_01_ensembl.html   |  4 ++--
 2 files changed, 7 insertions(+), 13 deletions(-)

diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
index 18e82fd1..0db4201b 100644
--- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
+++ b/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd
@@ -147,16 +147,10 @@ From here you can customize this analysis example to fit your own scientific que
 
 # Obtaining Annotation for Ensembl IDs - Microarray
 
-Ensembl IDs can be used to obtain various different annotations at the 
-gene/transcript level.
-Although this example script uses Ensembl IDs from Mouse, (Mus musculus),
-to obtain gene symbols this script can be easily converted for use with different 
-species or annotation types eg protein IDs, gene ontology, accession 
-numbers. 
-
-For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is 
-written, it must be replaced with the respective species abbreviation eg
-for Homo sapiens `org.Hs.eg.db` or `Hs` would be used.
+Ensembl IDs can be used to obtain various different annotations at the gene/transcript level and are the gene identifiers used by refine.bio.
+Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.
+
+For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used.
 A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData) [@annotation-packages].
 
 ## Install libraries
@@ -236,7 +230,7 @@ mapped_df <-
     "Symbols" = mapIds(
       org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
       keys = df$Gene,
-      column = "SYMBOL",
+      column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
       keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data
     ),
     df
diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-convert_microarray_01_ensembl.html
index 80fcd693..d18c59b1 100644
--- a/02-microarray/gene-id-convert_microarray_01_ensembl.html
+++ b/02-microarray/gene-id-convert_microarray_01_ensembl.html
@@ -1771,7 +1771,7 @@ 
        3 Using a different refine.bio da

        4 Obtaining Annotation for Ensembl IDs - Microarray

        -

        Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

        +

        Ensembl IDs can be used to obtain various different annotations at the gene/transcript level and are the gene identifiers used by refine.bio. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

        For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).

        4.1 Install libraries

        @@ -1901,7 +1901,7 @@

        4.3 Map Ensembl IDs to gene symbo "Symbols" = mapIds( org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data keys = df$Gene, - column = "SYMBOL", + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data ), df From a0c076b30375ee775ef95ff679594f55611fecd1 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Tue, 15 Sep 2020 11:40:51 -0400 Subject: [PATCH 09/18] use `list` and `filter` in `mapIds()` function execution - rename the file and propagate change in `Snakefile` and `navbar.html` - update references - rearrange notebook and rerun Snakefile --- 01-getting-started/getting-started.html | 2 +- 02-microarray/00-intro-to-microarray.html | 2 +- .../clustering_microarray_01_heatmap.html | 2 +- ...dimension-reduction_microarray_01_pca.html | 2 +- ...imension-reduction_microarray_02_umap.html | 2 +- ...e-id-annotation_microarray_01_ensembl.Rmd} | 116 +++++--- ...-id-annotation_microarray_01_ensembl.html} | 255 +++++++++--------- 03-rnaseq/00-intro-to-rnaseq.html | 2 +- 03-rnaseq/clustering_rnaseq_01_heatmap.html | 2 +- .../differential-expression_rnaseq_01.html | 4 +- .../dimension-reduction_rnaseq_01_pca.html | 2 +- .../dimension-reduction_rnaseq_02_umap.html | 2 +- .../00-intro-to-advanced-topics.html | 2 +- Snakefile | 2 +- components/_navbar.html | 2 +- references.bib | 7 + 16 files changed, 232 insertions(+), 174 deletions(-) rename 02-microarray/{gene-id-convert_microarray_01_ensembl.Rmd => gene-id-annotation_microarray_01_ensembl.Rmd} (74%) rename 02-microarray/{gene-id-convert_microarray_01_ensembl.html => gene-id-annotation_microarray_01_ensembl.html} (99%) diff --git a/01-getting-started/getting-started.html b/01-getting-started/getting-started.html index 09e70d2c..dcad5bb2 100644 --- a/01-getting-started/getting-started.html +++ b/01-getting-started/getting-started.html @@ -1614,7 +1614,7 @@
      • Differential Expression
      • Dimension Reduction - PCA
      • Dimension Reduction - UMAP
        • -
      • Gene ID Conversion
        • +
      • Ensembl Gene ID Annotation

      • diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index 88abe0b5..b331390c 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -1614,7 +1614,7 @@
    • Differential Expression
    • Dimension Reduction - PCA
    • Dimension Reduction - UMAP
      • -
    • Gene ID Conversion
      • +
    • Ensembl Gene ID Annotation
    diff --git a/02-microarray/clustering_microarray_01_heatmap.html b/02-microarray/clustering_microarray_01_heatmap.html index 09fcc202..7381e63c 100644 --- a/02-microarray/clustering_microarray_01_heatmap.html +++ b/02-microarray/clustering_microarray_01_heatmap.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/02-microarray/dimension-reduction_microarray_01_pca.html b/02-microarray/dimension-reduction_microarray_01_pca.html index 16691229..aa93e0f1 100644 --- a/02-microarray/dimension-reduction_microarray_01_pca.html +++ b/02-microarray/dimension-reduction_microarray_01_pca.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/02-microarray/dimension-reduction_microarray_02_umap.html b/02-microarray/dimension-reduction_microarray_02_umap.html index d7a874af..94fc977b 100644 --- a/02-microarray/dimension-reduction_microarray_02_umap.html +++ b/02-microarray/dimension-reduction_microarray_02_umap.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd similarity index 74% rename from 02-microarray/gene-id-convert_microarray_01_ensembl.Rmd rename to 02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index 0db4201b..0c8f3efd 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -11,7 +11,7 @@ output: # Purpose of this analysis -The purpose of this notebook is to provide an example annotation workflow for microarray data obtained from refine.bio using Bioconductor annotation packages. +The purpose of this notebook is to provide an example of mapping gene ids for microarray data obtained from refine.bio using `AnnotationDbi` packages. ⬇️ [**Jump to the analysis code**](#analysis) ⬇️ @@ -82,7 +82,7 @@ You will get an email when it is ready. For this example analysis, we will use this [mouse glioma stem cells dataset](https://www.refine.bio/experiments/GSE13490/cancer-stem-cells-are-enriched-in-the-side-population-cells-in-a-mouse-model-of-glioma). -The data that we downloaded from refine.bio for this analysis has 15 microarray mouse glioma model samples. +This dataset has 15 microarray mouse glioma model samples. The samples were obtained from parental biological replicates and from resistant sub-line biological replicates that were transplanted into recipient mice. ## Place the dataset in your new `data/` folder @@ -96,7 +96,7 @@ Double clicking should unzip this for you and create a folder of the same name. For more details on the contents of this folder see [these docs on refine.bio](http://docs.refine.bio/en/latest/main_text.html#downloadable-files). The `` folder has the data and metadata TSV files you will need for this example analysis. -Experiment accession ids usually look something like `GSE1235` or `SRP12345`. +Experiment accession IDs usually look something like `GSE1235` or `SRP12345`. Copy and paste the `GSE13490` folder into your newly created `data/` folder. @@ -112,7 +112,7 @@ Your new analysis folder should contain: - A folder for `plots` (currently empty) - A folder for `results` (currently empty) -Your example analysis folder should now look something like this (except with respective experiment accession id and analysis notebook name you are using): +Your example analysis folder should now look something like this (except with respective experiment accession ID and analysis notebook name you are using): @@ -140,6 +140,13 @@ If you'd like to adapt an example analysis to use a different dataset from [refi We suggest saving plots and results to `plots/` and `results/` directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences. +Refine.bio data comes with gene level data with Ensembl IDs. +Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers. + +For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. +In the case of our [RNA-seq gene identifier annotation example notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd), a Zebrafish (Danio rerio) dataset is used, meaning `org.Dr.eg.db` or `Dr` would also need to be used there. +A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData) [@annotation-packages]. + ***   @@ -147,11 +154,8 @@ From here you can customize this analysis example to fit your own scientific que # Obtaining Annotation for Ensembl IDs - Microarray -Ensembl IDs can be used to obtain various different annotations at the gene/transcript level and are the gene identifiers used by refine.bio. -Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers. - -For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. -A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData) [@annotation-packages]. +Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. +Let's get ready to use the Ensembl IDs from our mouse dataset to obtain the associated gene symbols. ## Install libraries @@ -194,7 +198,7 @@ df <- readr::read_tsv(file.path( data_dir, # Replace with path to your data file "GSE13490.tsv" # Replace with the name of your data file )) %>% - # Tuck away the Gene id column as rownames + # Tuck away the Gene ID column as rownames tibble::column_to_rownames("Gene") ``` @@ -217,41 +221,55 @@ df <- df %>% ## Map Ensembl IDs to gene symbols This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. -The `mapIDs()` function has a `multiVals` argument which denotes what to do when there are multiple mapped values for a single gene identifier. +The `mapIds()` function has a `multiVals` argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. -We will use the default behavior in the next chunk, but it is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. +It is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use `?mapIds` to see more options or strategies. +In the next chunk, we will run the `mapIds()` function and supply the `multiVals` argument with the `"list"` option in order to get a large list with all the mapped values found for each gene identifier. ```{r} # Map ensembl IDs to their associated gene symbols -mapped_df <- - data.frame( - "Symbols" = mapIds( - org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data - keys = df$Gene, - column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to - keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data - ), - df - ) +mapped_list <- mapIds( + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data + keys = df$Gene, + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "list" +) ``` -## Explore mapped data frame +## Explore mapped object + +Now, let's take a look at our mapped object to see how the mapping went. + +```{r} +# Let's use the `head()` function to take a preview at our mapped list +head(mapped_list) +``` -Now, let's take a look at our mapped data frame to see how the mapping went. +It looks like we have gene symbols that were successfully mapped to the Ensembl IDs we provided. +However, the data is now in a `list` object, making it a little more difficult to explore. +We are going to turn our list object into a data frame object in the next chunk. + +```{r} +# Let's make our object a bit more manageable for exploration by turning it into a data frame using the `reshape2::melt()` function +mapped_df <- mapped_list %>% + reshape2::melt() +``` + +Now let's take a peek at our data frame. ```{r} -# Let's use the `head()` function to take a preview at our mapped data frame head(mapped_df) ``` -We can see that our data frame has a new column `Symbols`. -Let's get a summary of the values returned in the `Symbols` column of our mapped data frame. +We can see that our data frame has a new column `value`. +Let's get a summary of the gene symbols returned in the `value` column of our mapped data frame. ```{r} -# We can use the `summary()` function to get a better idea of the distribution of values in the `Symbols` column -summary(mapped_df$Symbols) +# We can use the `summary()` function to get a better idea of the distribution of symbols in the `value` column +summary(mapped_df$value) ``` There are 998 NA's in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. @@ -262,27 +280,50 @@ Now let's check to see if we have any genes that were mapped to multiple symbols ```{r} multi_mapped <- mapped_df %>% - # Let's isolate our `Symbols` and `Gene` columns - dplyr::select(Symbols, Gene) %>% - # Now we are going to group by the Ensembl ID's in the `Gene` column - dplyr::group_by(Gene) %>% + # Let's group by the Ensembl ID's in the `L1` column + dplyr::group_by(L1) %>% # Create a new variable containing the number of symbols mapped to each ID dplyr::mutate(gene_symbol_count = n()) %>% # Arrange by the genes with the highest number of symbols mapped - dplyr::arrange(gene_symbol_count) + dplyr::arrange(desc(gene_symbol_count)) %>% + # Filter to include only the rows with multi mappings + dplyr::filter(gene_symbol_count > 1) # Let's look at the first 6 rows of our `multi_mapped` object head(multi_mapped) ``` -Looks like we do not have any cases of genes with multiple mappings in this particular dataset. -This is not the case for every dataset, therefore this step is good practice when mapping between gene identifiers. +Looks like we have some cases where 3 gene symbols mapped to a single Ensembl ID. +We have a total of 130 out of 17984 Ensembl IDs with multiple mappings to gene symbols. +We are not too worried about the 130 IDs with multiple mappings, so we will filter them out for the purpose of having 1:1 mappings for our downstream analysis. + +## Map Ensembl IDs to gene symbols -- filtering out multi mappings + +The next code chunk will rerun the `mapIds()` function, this time supplying the `"filter"` option to the `multiVals` argument. +This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. +Use `?mapIds` to see more options or strategies. + + +```{r} +# Map ensembl IDs to their associated gene symbols +filtered_mapped_df <- mapIds( + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data + keys = df$Gene, + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "filter" +) %>% + # Turn our list into a data frame + reshape2::melt() %>% + # Now let's give the `value` column a name that is more relevant to the values it holds + dplyr::rename("Symbol" = "value") +``` ## Write mapped results to file ```{r Write to file} # Write mapped and annotated data frame to output file -readr::write_tsv(mapped_df, file.path( +readr::write_tsv(filtered_mapped_df, file.path( results_dir, "GSE13490_Gene_Symbols.tsv" # Replace with a relevant output file name )) @@ -293,6 +334,7 @@ readr::write_tsv(mapped_df, file.path( - Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020] - [More on Annotation mapping functions](https://www.bioconductor.org/packages/release/bioc/vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf) [@Edwards2020] +- See our [RNA-seq gene ID conversion notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd) as another applicable example, since the steps for this workflow do not change with technology [@gene-id-annotation-rna-seq]. # Session info diff --git a/02-microarray/gene-id-convert_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html similarity index 99% rename from 02-microarray/gene-id-convert_microarray_01_ensembl.html rename to 02-microarray/gene-id-annotation_microarray_01_ensembl.html index d18c59b1..17048e43 100644 --- a/02-microarray/gene-id-convert_microarray_01_ensembl.html +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • @@ -1666,7 +1666,7 @@

    September 2020

    1 Purpose of this analysis

    -

    The purpose of this notebook is to provide an example annotation workflow for microarray data obtained from refine.bio using Bioconductor annotation packages.

    +

    The purpose of this notebook is to provide an example of mapping gene ids for microarray data obtained from refine.bio using AnnotationDbi packages.

    ⬇️ Jump to the analysis code ⬇️

    @@ -1716,14 +1716,14 @@

    2.3 Obtain the dataset from refin

    2.4 About the dataset we are using for this example

    For this example analysis, we will use this mouse glioma stem cells dataset.

    -

    The data that we downloaded from refine.bio for this analysis has 15 microarray mouse glioma model samples. The samples were obtained from parental biological replicates and from resistant sub-line biological replicates that were transplanted into recipient mice.

    +

    This dataset has 15 microarray mouse glioma model samples. The samples were obtained from parental biological replicates and from resistant sub-line biological replicates that were transplanted into recipient mice.

    2.5 Place the dataset in your new data/ folder

    refine.bio will send you a download button in the email when it is ready. Follow the prompt to download a zip file that has a name with a series of letters and numbers and ends in .zip. Double clicking should unzip this for you and create a folder of the same name.

    For more details on the contents of this folder see these docs on refine.bio.

    -

    The <experiment_accession_id> folder has the data and metadata TSV files you will need for this example analysis. Experiment accession ids usually look something like GSE1235 or SRP12345.

    +

    The <experiment_accession_id> folder has the data and metadata TSV files you will need for this example analysis. Experiment accession IDs usually look something like GSE1235 or SRP12345.

    Copy and paste the GSE13490 folder into your newly created data/ folder.

    @@ -1746,7 +1746,7 @@

    2.6 Check out our file structure!
  • A folder for results (currently empty)
    • -

    Your example analysis folder should now look something like this (except with respective experiment accession id and analysis notebook name you are using):

    +

    Your example analysis folder should now look something like this (except with respective experiment accession ID and analysis notebook name you are using):

    In order for our example here to run without a hitch, we need these files to be in these locations so we’ve constructed a test to check before we get started with the analysis. Run this chunk to double check that your files are in the right place.

    # Define the file path to the data directory
@@ -1765,14 +1765,15 @@ 
    2.6 Check out our file structure!
 
    
 
    3 Using a different refine.bio dataset with this analysis?
    
 
    If you’d like to adapt an example analysis to use a different dataset from refine.bio, we recommend placing the files in the data/ directory you created and changing the filenames and paths in the notebook to match these files (we’ve put comments to signify where you would need to change the code). We suggest saving plots and results to plots/ and results/ directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences.
    
+
    Refine.bio data comes with gene level data with Ensembl IDs. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.
    
+
    For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. In the case of our RNA-seq gene identifier annotation example notebook, a Zebrafish (Danio rerio) dataset is used, meaning org.Dr.eg.db or Dr would also need to be used there. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).
    
 
    
 
 
     
    
 
    
 
    
 
    4 Obtaining Annotation for Ensembl IDs - Microarray
    
-
    Ensembl IDs can be used to obtain various different annotations at the gene/transcript level and are the gene identifiers used by refine.bio. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.
    
-
    For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).
    
+
    Ensembl IDs can be used to obtain various different annotations at the gene/transcript level. Let’s get ready to use the Ensembl IDs from our mouse dataset to obtain the associated gene symbols.
    
 
    
 
    4.1 Install libraries
    
 
    See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.
    
@@ -1859,7 +1860,7 @@ 
    4.2 Import and set up data
    
   data_dir, # Replace with path to your data file
   "GSE13490.tsv" # Replace with the name of your data file
 )) %>%
-  # Tuck away the Gene id column as rownames
+  # Tuck away the Gene ID column as rownames
   tibble::column_to_rownames("Gene")
    ## Parsed with column specification:
 ## cols(
@@ -1894,127 +1895,130 @@ 
    4.2 Import and set up data

    4.3 Map Ensembl IDs to gene symbols

    -

    This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIDs() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. We will use the default behavior in the next chunk, but it is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use ?mapIds to see more options or strategies.

    +

    This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIds() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. It is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use ?mapIds to see more options or strategies.

    +

    In the next chunk, we will run the mapIds() function and supply the multiVals argument with the "list" option in order to get a large list with all the mapped values found for each gene identifier.

    # Map ensembl IDs to their associated gene symbols
-mapped_df <-
-  data.frame(
-    "Symbols" = mapIds(
-      org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
-      keys = df$Gene,
-      column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
-      keytype = "ENSEMBL" # Replace with the type of gene identifiers in your data
-    ),
-    df
-  )
    +mapped_list <- mapIds( + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data + keys = df$Gene, + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "list" +)     
    ## 'select()' returned 1:many mapping between keys and columns
    -
    -

    4.4 Explore mapped data frame

    -

    Now, let’s take a look at our mapped data frame to see how the mapping went.

    -
    # Let's use the `head()` function to take a preview at our mapped data frame
-head(mapped_df)
    -
    ##                    Symbols               Gene GSM340068   GSM340072   GSM340071
-## ENSMUSG00000000001   Gnai3 ENSMUSG00000000001 9.0936148  7.99950284  8.95697104
-## ENSMUSG00000000003    Pbsn ENSMUSG00000000003 0.1158669 -0.01806812  0.33961890
-## ENSMUSG00000000028   Cdc45 ENSMUSG00000000028 4.0955659  4.08404318  6.40406029
-## ENSMUSG00000000031     H19 ENSMUSG00000000031 3.6377112  1.99403634  2.20337978
-## ENSMUSG00000000037   Scml2 ENSMUSG00000000037 0.3255582  2.50103467  0.87962948
-## ENSMUSG00000000049    Apoh ENSMUSG00000000049 0.9238867  0.90178719 -0.07378849
-##                     GSM340075 GSM340073 GSM340078 GSM340076  GSM340069
-## ENSMUSG00000000001 9.57407861 7.4204583 8.0817096 9.0049117 10.0229721
-## ENSMUSG00000000003 0.05642225 0.1751004 0.1402899 0.1945611  0.1729683
-## ENSMUSG00000000028 5.39607105 4.4854625 4.3000140 2.6582618  5.0904650
-## ENSMUSG00000000031 1.11520094 1.1794732 3.1565971 1.0680197 10.1382983
-## ENSMUSG00000000037 0.93308509 2.1829868 2.2657694 1.2691216  0.4042593
-## ENSMUSG00000000049 0.08114104 0.6793026 0.9798333 0.9241858  0.8410028
-##                    GSM340065    GSM340070  GSM340067   GSM340074 GSM340066
-## ENSMUSG00000000001 7.8532377  9.214928841 5.54997452 9.654812590 8.2018749
-## ENSMUSG00000000003 0.2580271 -0.001017382 0.01480985 0.546946872 0.2348428
-## ENSMUSG00000000028 4.7374842  5.148726742 2.69213997 4.780000261 3.0079289
-## ENSMUSG00000000031 1.9280822  3.169991848 0.19701060 0.954261673 0.7830665
-## ENSMUSG00000000037 2.1511658  0.762200101 0.82645739 0.781790693 1.3032066
-## ENSMUSG00000000049 0.3368805  0.107442284 1.63785952 0.003141663 0.5640828
-##                    GSM340064  GSM340077
-## ENSMUSG00000000001 7.8776291 6.78553772
-## ENSMUSG00000000003 0.2198192 0.04734498
-## ENSMUSG00000000028 5.5948725 6.10847476
-## ENSMUSG00000000031 3.2268280 0.80851073
-## ENSMUSG00000000037 1.4112563 1.74052445
-## ENSMUSG00000000049 0.5473140 0.08018819
    -

    We can see that our data frame has a new column Symbols. Let’s get a summary of the values returned in the Symbols column of our mapped data frame.

    -
    # We can use the `summary()` function to get a better idea of the distribution of values in the `Symbols` column
-summary(mapped_df$Symbols)
    -
    ##          Pms2 0610005C13Rik 0610009B22Rik 0610009L18Rik 0610010F05Rik 
-##             2             1             1             1             1 
-## 0610012G03Rik 0610030E20Rik 0610038B21Rik 0610039K10Rik 0610040B10Rik 
-##             1             1             1             1             1 
-## 0610040F04Rik 0610040J01Rik 1110002E22Rik 1110002L01Rik 1110004F10Rik 
-##             1             1             1             1             1 
-## 1110006O24Rik 1110008L16Rik 1110008P14Rik 1110015O18Rik 1110017D15Rik 
-##             1             1             1             1             1 
-## 1110019D14Rik 1110020A21Rik 1110028F18Rik 1110032A03Rik 1110032F04Rik 
-##             1             1             1             1             1 
-## 1110038F14Rik 1110046J04Rik 1110051M20Rik 1110059E24Rik 1110059G10Rik 
-##             1             1             1             1             1 
-## 1110065P20Rik 1190005I06Rik 1300002E11Rik 1300017J02Rik 1500009C09Rik 
-##             1             1             1             1             1 
-## 1500009L16Rik 1500011B03Rik 1500015A07Rik 1500015O10Rik 1600002K03Rik 
-##             1             1             1             1             1 
-## 1600010M07Rik 1600012H06Rik 1600014C10Rik 1600014C23Rik 1600015I10Rik 
-##             1             1             1             1             1 
-## 1600020E01Rik 1600023N17Rik 1600025M17Rik 1600029I14Rik 1600029O15Rik 
-##             1             1             1             1             1 
-## 1700001C02Rik 1700001C19Rik 1700001G11Rik 1700001G17Rik 1700001K19Rik 
-##             1             1             1             1             1 
-## 1700001L19Rik 1700001O22Rik 1700001P01Rik 1700003D09Rik 1700003E16Rik 
-##             1             1             1             1             1 
-## 1700003F12Rik 1700003G18Rik 1700003H04Rik 1700003P14Rik 1700006A11Rik 
-##             1             1             1             1             1 
-## 1700007F19Rik 1700007J10Rik 1700007K13Rik 1700007L15Rik 1700008J07Rik 
-##             1             1             1             1             1 
-## 1700008K24Rik 1700008O03Rik 1700008P02Rik 1700009J07Rik 1700009N14Rik 
-##             1             1             1             1             1 
-## 1700010B08Rik 1700010I14Rik 1700011B04Rik 1700011L22Rik 1700011M02Rik 
-##             1             1             1             1             1 
-## 1700012A03Rik 1700012B07Rik 1700012B09Rik 1700012D01Rik 1700012D14Rik 
-##             1             1             1             1             1 
-## 1700012I11Rik 1700012P22Rik 1700013F07Rik 1700013G24Rik 1700013H16Rik 
-##             1             1             1             1             1 
-## 1700015F17Rik 1700015G11Rik 1700016A09Rik 1700016C15Rik 1700016D06Rik 
-##             1             1             1             1             1 
-## 1700016H13Rik 1700016K19Rik 1700016P04Rik       (Other)          NA's 
-##             1             1             1         16821           998
    +
    +

    4.4 Explore mapped object

    +

    Now, let’s take a look at our mapped object to see how the mapping went.

    +
    # Let's use the `head()` function to take a preview at our mapped list
+head(mapped_list)
    +
    ## $ENSMUSG00000000001
+## [1] "Gnai3"
+## 
+## $ENSMUSG00000000003
+## [1] "Pbsn"
+## 
+## $ENSMUSG00000000028
+## [1] "Cdc45"
+## 
+## $ENSMUSG00000000031
+## [1] "H19"
+## 
+## $ENSMUSG00000000037
+## [1] "Scml2"
+## 
+## $ENSMUSG00000000049
+## [1] "Apoh"
    +

    It looks like we have gene symbols that were successfully mapped to the Ensembl IDs we provided. However, the data is now in a list object, making it a little more difficult to explore. We are going to turn our list object into a data frame object in the next chunk.

    +
    # Let's make our object a bit more manageable for exploration by turning it into a data frame using the `reshape2::melt()` function
+mapped_df <- mapped_list %>%
+  reshape2::melt()
    +

    Now let’s take a peek at our data frame.

    +
    head(mapped_df)
    +
    ##   value                 L1
+## 1 Gnai3 ENSMUSG00000000001
+## 2  Pbsn ENSMUSG00000000003
+## 3 Cdc45 ENSMUSG00000000028
+## 4   H19 ENSMUSG00000000031
+## 5 Scml2 ENSMUSG00000000037
+## 6  Apoh ENSMUSG00000000049
    +

    We can see that our data frame has a new column value. Let’s get a summary of the gene symbols returned in the value column of our mapped data frame.

    +
    # We can use the `summary()` function to get a better idea of the distribution of symbols in the `value` column
+summary(mapped_df$value)
    +
    ##  Gm13023   Nudt10     Pms2    Gnai3     Pbsn    Cdc45      H19    Scml2 
+##        2        2        2        1        1        1        1        1 
+##     Apoh     Narf     Cav2     Klf6    Scmh1    Cox5a     Tbx2     Tbx4 
+##        1        1        1        1        1        1        1        1 
+##     Zfy2     Ngfr     Wnt3    Wnt9a      Fer     Xpo6     Tfe3    Axin2 
+##        1        1        1        1        1        1        1        1 
+##    Brat1    Gna12 Slc22a18   Itgb2l    Igsf5   Pih1d2     Dlat     Sdhd 
+##        1        1        1        1        1        1        1        1 
+##    Fgf23     Fgf6    Ccnd2   Gpr107    Nalcn   Btbd17    Slfn4       Th 
+##        1        1        1        1        1        1        1        1 
+##     Ins2   Scnn1g     Drp2  Tspan32     Lhx2   Clec2g     Gmpr    Glra1 
+##        1        1        1        1        1        1        1        1 
+##     Mid2   Trim25     Dgke   Scpep1      Mnt    Itgb2    Hddc2  Tpd52l1 
+##        1        1        1        1        1        1        1        1 
+##     Pemt     Cdh1     Cdh4    Ckmt1    Bcl6b  Clec10a   Alox12    Arvcf 
+##        1        1        1        1        1        1        1        1 
+##     Comt     Rtca      Dbt   Dazap2    Mcts1     Rem1    Rnf17 Trappc10 
+##        1        1        1        1        1        1        1        1 
+##     Ccm2      Wap    Tbrg4  Tmprss2      Mx1      Fap      Gcg   Ndufa9 
+##        1        1        1        1        1        1        1        1 
+##    Egfl6      Lck    Tssk3  Cttnbp2   Galnt1     Myf5    Mkrn2    Pparg 
+##        1        1        1        1        1        1        1        1 
+##     Raf1   Gm4532    Sept1    Pdgfb   Acvrl1    Grasp   Acvr1b   Tom1l2 
+##        1        1        1        1        1        1        1        1 
+##    Gpa33  Zfp385a  (Other)     NA's 
+##        1        1    16885      998

    There are 998 NA’s in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. If nothing is mapping to your gene identifier, it is possible that the function was executed incorrectly or a different gene identifier should be used. In some cases, this behavior is expected, but it is always good to be aware of how many genes you are potentially “losing” if you rely on this new gene identifier you’ve mapped to for downstream analyses.

    Now let’s check to see if we have any genes that were mapped to multiple symbols.

    multi_mapped <- mapped_df %>%
-  # Let's isolate our `Symbols` and `Gene` columns
-  dplyr::select(Symbols, Gene) %>%
-  # Now we are going to group by the Ensembl ID's in the `Gene` column
-  dplyr::group_by(Gene) %>%
+  # Let's group by the Ensembl ID's in the `L1` column
+  dplyr::group_by(L1) %>%
   # Create a new variable containing the number of symbols mapped to each ID
   dplyr::mutate(gene_symbol_count = n()) %>%
   # Arrange by the genes with the highest number of symbols mapped
-  dplyr::arrange(gene_symbol_count)
    + dplyr::arrange(desc(gene_symbol_count)) %>% + # Filter to include only the rows with multi mappings + dplyr::filter(gene_symbol_count > 1)     
    ## Warning: Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()`.
 ## This warning is displayed once per session.
    # Let's look at the first 6 rows of our `multi_mapped` object
 head(multi_mapped)
    ## # A tibble: 6 x 3
-## # Groups:   Gene [6]
-##   Symbols Gene               gene_symbol_count
-##   <fct>   <chr>                          <int>
-## 1 Gnai3   ENSMUSG00000000001                 1
-## 2 Pbsn    ENSMUSG00000000003                 1
-## 3 Cdc45   ENSMUSG00000000028                 1
-## 4 H19     ENSMUSG00000000031                 1
-## 5 Scml2   ENSMUSG00000000037                 1
-## 6 Apoh    ENSMUSG00000000049                 1
    -

    Looks like we do not have any cases of genes with multiple mappings in this particular dataset. This is not the case for every dataset, therefore this step is good practice when mapping between gene identifiers.

    +## # Groups: L1 [2] +## value L1 gene_symbol_count +## <fct> <chr> <int> +## 1 Rpl23 ENSMUSG00000071415 3 +## 2 LOC100044627 ENSMUSG00000071415 3 +## 3 LOC100862455 ENSMUSG00000071415 3 +## 4 Ppp2r3d ENSMUSG00000093803 3 +## 5 LOC108167320 ENSMUSG00000093803 3 +## 6 LOC108167694 ENSMUSG00000093803 3 +

    Looks like we have some cases where 3 gene symbols mapped to a single Ensembl ID. We have a total of 130 out of 17984 Ensembl IDs with multiple mappings to gene symbols. We are not too worried about the 130 IDs with multiple mappings, so we will filter them out for the purpose of having 1:1 mappings for our downstream analysis.

    +
    +
    +

    4.5 Map Ensembl IDs to gene symbols – filtering out multi mappings

    +

    The next code chunk will rerun the mapIds() function, this time supplying the "filter" option to the multiVals argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use ?mapIds to see more options or strategies.

    +
    # Map ensembl IDs to their associated gene symbols
+filtered_mapped_df <- mapIds(
+  org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
+  keys = df$Gene,
+  column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
+  keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
+  multiVals = "filter"
+) %>%
+  # Turn our list into a data frame
+  reshape2::melt() %>%
+  # Now let's give the `value` column a name that is more relevant to the values it holds
+  dplyr::rename("Symbol" = "value")
    +
    ## 'select()' returned 1:many mapping between keys and columns
    -

    4.5 Write mapped results to file

    +

    4.6 Write mapped results to file

    # Write mapped and annotated data frame to output file
-readr::write_tsv(mapped_df, file.path(
+readr::write_tsv(filtered_mapped_df, file.path(
   results_dir,
   "GSE13490_Gene_Symbols.tsv" # Replace with a relevant output file name
 ))
    @@ -2025,6 +2029,7 @@

    5 Resources for further learning<

    @@ -2053,17 +2058,18 @@

    6 Session info

    ## [7] BiocGenerics_0.31.5 optparse_1.6.2 ## ## loaded via a namespace (and not attached): -## [1] Rcpp_1.0.4.6 pillar_1.4.4 compiler_3.6.1 tools_3.6.1 -## [5] bit_1.1-14 digest_0.6.25 memoise_1.1.0 RSQLite_2.1.2 -## [9] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.1 pkgconfig_2.0.3 -## [13] rlang_0.4.6 DBI_1.0.0 cli_2.0.2 rstudioapi_0.11 -## [17] yaml_2.2.1 xfun_0.14 dplyr_0.8.3 withr_2.2.0 -## [21] styler_1.1.1 stringr_1.4.0 knitr_1.28 vctrs_0.3.1 -## [25] hms_0.5.3 tidyselect_0.2.5 bit64_0.9-7 getopt_1.20.3 -## [29] glue_1.4.1 R6_2.4.1 fansi_0.4.1 rmarkdown_1.14 -## [33] blob_1.2.0 readr_1.3.1 purrr_0.3.4 rematch2_2.1.0 -## [37] backports_1.1.7 ellipsis_0.3.1 htmltools_0.3.6 assertthat_0.2.1 -## [41] utf8_1.1.4 stringi_1.4.6 crayon_1.3.4 +## [1] Rcpp_1.0.4.6 plyr_1.8.4 pillar_1.4.4 compiler_3.6.1 +## [5] tools_3.6.1 bit_1.1-14 digest_0.6.25 memoise_1.1.0 +## [9] RSQLite_2.1.2 evaluate_0.14 lifecycle_0.2.0 tibble_3.0.1 +## [13] pkgconfig_2.0.3 rlang_0.4.6 DBI_1.0.0 cli_2.0.2 +## [17] rstudioapi_0.11 yaml_2.2.1 xfun_0.14 dplyr_0.8.3 +## [21] withr_2.2.0 styler_1.1.1 stringr_1.4.0 knitr_1.28 +## [25] vctrs_0.3.1 hms_0.5.3 tidyselect_0.2.5 bit64_0.9-7 +## [29] getopt_1.20.3 glue_1.4.1 R6_2.4.1 fansi_0.4.1 +## [33] rmarkdown_1.14 reshape2_1.4.3 blob_1.2.0 readr_1.3.1 +## [37] purrr_0.3.4 rematch2_2.1.0 backports_1.1.7 ellipsis_0.3.1 +## [41] htmltools_0.3.6 assertthat_0.2.1 utf8_1.1.4 stringi_1.4.6 +## [45] crayon_1.3.4

    Carlson M., 2019 Genome wide annotation for mouse

    @@ -2071,6 +2077,9 @@

    6 Session info

    Carlson M., 2020 AnnotationDbi: Introduction to bioconductor annotation packages

    +
    +

    CCDL, 2020 Obtaining annotation for ensembl ids - rna-seq.

    +

    Edwards S. M., 2020 Annotation mapping functions.

    diff --git a/03-rnaseq/00-intro-to-rnaseq.html b/03-rnaseq/00-intro-to-rnaseq.html index bf0d9da7..e74f5560 100644 --- a/03-rnaseq/00-intro-to-rnaseq.html +++ b/03-rnaseq/00-intro-to-rnaseq.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/03-rnaseq/clustering_rnaseq_01_heatmap.html b/03-rnaseq/clustering_rnaseq_01_heatmap.html index 3d1d8d41..8cafa6aa 100644 --- a/03-rnaseq/clustering_rnaseq_01_heatmap.html +++ b/03-rnaseq/clustering_rnaseq_01_heatmap.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/03-rnaseq/differential-expression_rnaseq_01.html b/03-rnaseq/differential-expression_rnaseq_01.html index 134f9459..125c3606 100644 --- a/03-rnaseq/differential-expression_rnaseq_01.html +++ b/03-rnaseq/differential-expression_rnaseq_01.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • @@ -2055,7 +2055,7 @@

    4.6 Run differential expression a

    4.6.1 Check results by plotting one gene

    To double check what a differentially expressed gene looks like, we can plot one with DESeq2::plotCounts() function.

    plotCounts(ddset, gene = "ENSG00000196074", intgroup = "asxl_mutation_status")
    -

    +

    The mutation group samples have higher expression of this gene than the control group, which helps assure us that the results are showing us what we are looking for.

    diff --git a/03-rnaseq/dimension-reduction_rnaseq_01_pca.html b/03-rnaseq/dimension-reduction_rnaseq_01_pca.html index 684038a1..84e98fa6 100644 --- a/03-rnaseq/dimension-reduction_rnaseq_01_pca.html +++ b/03-rnaseq/dimension-reduction_rnaseq_01_pca.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/03-rnaseq/dimension-reduction_rnaseq_02_umap.html b/03-rnaseq/dimension-reduction_rnaseq_02_umap.html index 9648c13c..ce5177eb 100644 --- a/03-rnaseq/dimension-reduction_rnaseq_02_umap.html +++ b/03-rnaseq/dimension-reduction_rnaseq_02_umap.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/04-advanced-topics/00-intro-to-advanced-topics.html b/04-advanced-topics/00-intro-to-advanced-topics.html index 62cbcc64..65b1fae3 100644 --- a/04-advanced-topics/00-intro-to-advanced-topics.html +++ b/04-advanced-topics/00-intro-to-advanced-topics.html @@ -1614,7 +1614,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/Snakefile b/Snakefile index 3c17484e..418343ee 100644 --- a/Snakefile +++ b/Snakefile @@ -6,7 +6,7 @@ rule target: "02-microarray/clustering_microarray_01_heatmap.html", "02-microarray/dimension-reduction_microarray_01_pca.html", "02-microarray/dimension-reduction_microarray_02_umap.html", - "02-microarray/gene-id-convert_microarray_01_ensembl.html", + "02-microarray/gene-id-annotation_microarray_01_ensembl.html", "03-rnaseq/clustering_rnaseq_01_heatmap.html", "03-rnaseq/differential-expression_rnaseq_01.html", "03-rnaseq/00-intro-to-rnaseq.html", diff --git a/components/_navbar.html b/components/_navbar.html index 12032545..cb0f2fdb 100644 --- a/components/_navbar.html +++ b/components/_navbar.html @@ -26,7 +26,7 @@
  • Differential Expression
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Gene ID Conversion
    • +
  • Ensembl Gene ID Annotation
    • diff --git a/references.bib b/references.bib index 507b72fa..e02284e0 100644 --- a/references.bib +++ b/references.bib @@ -63,6 +63,13 @@ @Website{Freytag2019 url = {https://rpubs.com/Saskia/520216}, } +@Manual{gene-id-annotation-rna-seq, + title = {Obtaining Annotation for Ensembl IDs - RNA-seq}, + author = {CCDL}, + year = {2020}, + url = {https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd}, +} + @Manual{Gonzalez2014, title = {Statistical analysis of RNA-Seq data}, author = {Ignacio Gonzalez}, From 15dbff1a050dadfe71293d7be15ae97792b0a2c1 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Tue, 15 Sep 2020 11:51:11 -0400 Subject: [PATCH 10/18] fix some formatting --- ...ne-id-annotation_microarray_01_ensembl.Rmd | 14 +++++++------ ...e-id-annotation_microarray_01_ensembl.html | 20 +++++++++---------- 2 files changed, 18 insertions(+), 16 deletions(-) diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index 0c8f3efd..63edb0c9 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -11,7 +11,7 @@ output: # Purpose of this analysis -The purpose of this notebook is to provide an example of mapping gene ids for microarray data obtained from refine.bio using `AnnotationDbi` packages. +The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using `AnnotationDbi` packages. ⬇️ [**Jump to the analysis code**](#analysis) ⬇️ @@ -140,7 +140,7 @@ If you'd like to adapt an example analysis to use a different dataset from [refi We suggest saving plots and results to `plots/` and `results/` directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences. -Refine.bio data comes with gene level data with Ensembl IDs. +refine.bio data comes with gene level data with Ensembl IDs. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers. For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. @@ -239,7 +239,7 @@ mapped_list <- mapIds( ) ``` -## Explore mapped object +## Explore gene ID conversion Now, let's take a look at our mapped object to see how the mapping went. @@ -273,8 +273,10 @@ summary(mapped_df$value) ``` There are 998 NA's in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. -If nothing is mapping to your gene identifier, it is possible that the function was executed incorrectly or a different gene identifier should be used. -In some cases, this behavior is expected, but it is always good to be aware of how many genes you are potentially "losing" if you rely on this new gene identifier you've mapped to for downstream analyses. +998 out of 17918 is not too bad a rate, in our opinion, but note that different gene identifier types will have different mapping rates and that is to be expected. +Regardless, it is always good to be aware of how many genes you are potentially "losing" if you rely on this new gene identifier you've mapped to for downstream analyses. + +However, if you have almost all NA's it is possible that the function was executed incorrectly or you may want to consider using a different gene identifier, if possible. Now let's check to see if we have any genes that were mapped to multiple symbols. @@ -283,7 +285,7 @@ multi_mapped <- mapped_df %>% # Let's group by the Ensembl ID's in the `L1` column dplyr::group_by(L1) %>% # Create a new variable containing the number of symbols mapped to each ID - dplyr::mutate(gene_symbol_count = n()) %>% + dplyr::mutate(gene_symbol_count = dplyr::n()) %>% # Arrange by the genes with the highest number of symbols mapped dplyr::arrange(desc(gene_symbol_count)) %>% # Filter to include only the rows with multi mappings diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html index 17048e43..8a3a1dc2 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.html +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html @@ -1666,7 +1666,7 @@

    September 2020

    1 Purpose of this analysis

    -

    The purpose of this notebook is to provide an example of mapping gene ids for microarray data obtained from refine.bio using AnnotationDbi packages.

    +

    The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using AnnotationDbi packages.

    ⬇️ Jump to the analysis code ⬇️

    @@ -1765,7 +1765,7 @@

    2.6 Check out our file structure!

    3 Using a different refine.bio dataset with this analysis?

    If you’d like to adapt an example analysis to use a different dataset from refine.bio, we recommend placing the files in the data/ directory you created and changing the filenames and paths in the notebook to match these files (we’ve put comments to signify where you would need to change the code). We suggest saving plots and results to plots/ and results/ directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences.

    -

    Refine.bio data comes with gene level data with Ensembl IDs. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

    +

    refine.bio data comes with gene level data with Ensembl IDs. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

    For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. In the case of our RNA-seq gene identifier annotation example notebook, a Zebrafish (Danio rerio) dataset is used, meaning org.Dr.eg.db or Dr would also need to be used there. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).

    @@ -1907,8 +1907,8 @@

    4.3 Map Ensembl IDs to gene symbo ) 
    ## 'select()' returned 1:many mapping between keys and columns

    -
    -

    4.4 Explore mapped object

    +
    +

    4.4 Explore gene ID conversion

    Now, let’s take a look at our mapped object to see how the mapping went.

    # Let's use the `head()` function to take a preview at our mapped list
 head(mapped_list)
    @@ -1971,20 +1971,20 @@

    4.4 Explore mapped object

    ## 1 1 1 1 1 1 1 1 ## Gpa33 Zfp385a (Other) NA's ## 1 1 16885 998 -

    There are 998 NA’s in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. If nothing is mapping to your gene identifier, it is possible that the function was executed incorrectly or a different gene identifier should be used. In some cases, this behavior is expected, but it is always good to be aware of how many genes you are potentially “losing” if you rely on this new gene identifier you’ve mapped to for downstream analyses.

    +

    There are 998 NA’s in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. 998 out of 17918 is not too bad a rate, in our opinion, but note that different gene identifier types will have different mapping rates and that is to be expected. Regardless, it is always good to be aware of how many genes you are potentially “losing” if you rely on this new gene identifier you’ve mapped to for downstream analyses.

    +

    However, if you have almost all NA’s it is possible that the function was executed incorrectly or you may want to consider using a different gene identifier, if possible.

    Now let’s check to see if we have any genes that were mapped to multiple symbols.

    multi_mapped <- mapped_df %>%
   # Let's group by the Ensembl ID's in the `L1` column
   dplyr::group_by(L1) %>%
   # Create a new variable containing the number of symbols mapped to each ID
-  dplyr::mutate(gene_symbol_count = n()) %>%
+  dplyr::mutate(gene_symbol_count = dplyr::n()) %>%
   # Arrange by the genes with the highest number of symbols mapped
   dplyr::arrange(desc(gene_symbol_count)) %>%
   # Filter to include only the rows with multi mappings
-  dplyr::filter(gene_symbol_count > 1)
    -
    ## Warning: Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()`.
-## This warning is displayed once per session.
    -
    # Let's look at the first 6 rows of our `multi_mapped` object
+  dplyr::filter(gene_symbol_count > 1)
+
+# Let's look at the first 6 rows of our `multi_mapped` object
 head(multi_mapped)
    ## # A tibble: 6 x 3
 ## # Groups:   L1 [2]

From a40cbae643a598de648949e5a1d2f09f4fcd9620 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Tue, 15 Sep 2020 11:54:14 -0400
Subject: [PATCH 11/18] fix spacing

---
 02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd | 1 -
 1 file changed, 1 deletion(-)

diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
index 63edb0c9..3cf4c0d7 100644
--- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
+++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
@@ -305,7 +305,6 @@ The next code chunk will rerun the `mapIds()` function, this time supplying the
 This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping.
 Use `?mapIds` to see more options or strategies.
 
-
 ```{r}
 # Map ensembl IDs to their associated gene symbols
 filtered_mapped_df <- mapIds(

From f4b3d6817828ba88cbd4323de07211d0a7aa6c57 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Tue, 15 Sep 2020 14:54:49 -0400
Subject: [PATCH 12/18] add a sanity check after obtaining new filtered df

---
 ...ne-id-annotation_microarray_01_ensembl.Rmd | 20 +++++++++++++++++++
 ...e-id-annotation_microarray_01_ensembl.html | 17 ++++++++++++++++
 2 files changed, 37 insertions(+)

diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
index 3cf4c0d7..f74e6bb8 100644
--- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
+++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd
@@ -320,6 +320,26 @@ filtered_mapped_df <- mapIds(
   dplyr::rename("Symbol" = "value")
 ```
 
+Let's copy the same code we used above to see if we have any multi mappings in our new data frame.
+
+```{r}
+filtered_mapped_df %>%
+  # First, we need to grab the Ensembl IDs from the rownames
+  tibble::rownames_to_column("Gene") %>%
+  # Let's group by the Ensembl ID's in the `L1` column
+  dplyr::group_by(Gene) %>%
+  # Create a new variable containing the number of symbols mapped to each ID
+  dplyr::mutate(gene_symbol_count = dplyr::n()) %>%
+  # Arrange by the genes with the highest number of symbols mapped
+  dplyr::arrange(desc(gene_symbol_count)) %>%
+  # Filter to include only the rows with multi mappings
+  dplyr::filter(gene_symbol_count > 1)
+```
+
+This new new `filtered_mapped_df` object is empty, as expected, since it has been filtered to include only 1:1 mappings.
+
+Now, let's write our filtered and mapped results to file!
+
 ## Write mapped results to file
 
 ```{r Write to file}
diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html
index 8a3a1dc2..99f25e4b 100644
--- a/02-microarray/gene-id-annotation_microarray_01_ensembl.html
+++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html
@@ -2014,6 +2014,23 @@ 
    4.5 Map Ensembl IDs to gene symbo
   # Now let's give the `value` column a name that is more relevant to the values it holds
   dplyr::rename("Symbol" = "value")
    ## 'select()' returned 1:many mapping between keys and columns
    +

    Let’s copy the same code we used above to see if we have any multi mappings in our new data frame.

    +
    filtered_mapped_df %>%
+  # First, we need to grab the Ensembl IDs from the rownames
+  tibble::rownames_to_column("Gene") %>%
+  # Let's group by the Ensembl ID's in the `L1` column
+  dplyr::group_by(Gene) %>%
+  # Create a new variable containing the number of symbols mapped to each ID
+  dplyr::mutate(gene_symbol_count = dplyr::n()) %>%
+  # Arrange by the genes with the highest number of symbols mapped
+  dplyr::arrange(desc(gene_symbol_count)) %>%
+  # Filter to include only the rows with multi mappings
+  dplyr::filter(gene_symbol_count > 1)
    +
    ## # A tibble: 0 x 3
+## # Groups:   Gene [0]
+## # … with 3 variables: Gene <chr>, Symbol <fct>, gene_symbol_count <int>
    +

    This new new filtered_mapped_df object is empty, as expected, since it has been filtered to include only 1:1 mappings.

    +

    Now, let’s write our filtered and mapped results to file!

    4.6 Write mapped results to file

    From b7399119c35e08901b17770ecfff993865ccc98e Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 16 Sep 2020 15:34:47 -0400 Subject: [PATCH 13/18] incorporate @cansavvy's review suggestions --- ...ne-id-annotation_microarray_01_ensembl.Rmd | 66 +++++--------- ...e-id-annotation_microarray_01_ensembl.html | 87 +++++++------------ 03-rnaseq/00-intro-to-rnaseq.html | 2 +- .../differential-expression_rnaseq_01.html | 4 +- references.bib | 7 -- 5 files changed, 59 insertions(+), 107 deletions(-) diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index f74e6bb8..1e43a2c2 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -141,10 +141,10 @@ We suggest saving plots and results to `plots/` and `results/` directories, resp From here you can customize this analysis example to fit your own scientific questions and preferences. refine.bio data comes with gene level data with Ensembl IDs. -Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers. +Although this example notebook uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this notebook can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers. For different species, wherever the abbreviation `org.Mm.eg.db` or `Mm` is written, it must be replaced with the respective species abbreviation eg for Homo sapiens `org.Hs.eg.db` or `Hs` would be used. -In the case of our [RNA-seq gene identifier annotation example notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd), a Zebrafish (Danio rerio) dataset is used, meaning `org.Dr.eg.db` or `Dr` would also need to be used there. +In the case of our [RNA-seq gene identifier annotation example notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-annotation_rnaseq_01_ensembl.html), a Zebrafish (Danio rerio) dataset is used, meaning `org.Dr.eg.db` or `Dr` would also need to be used there. A full list of the annotation R packages from Bioconductor is at this [link](https://bioconductor.org/packages/release/BiocViews.html#___AnnotationData) [@annotation-packages]. *** @@ -162,6 +162,7 @@ Let's get ready to use the Ensembl IDs from our mouse dataset to obtain the asso See our Getting Started page with [instructions for package installation](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#what-you-need-to-install) for a list of the other software you will need, as well as more tips and resources. In this analysis, we will be using the `org.Mm.eg.db` R package [@Carlson2019]. +[Other species can be used](#using-a-different-refinebio-dataset-with-this-analysis). ```{r} # Install the mouse package @@ -220,7 +221,6 @@ df <- df %>% ## Map Ensembl IDs to gene symbols -This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The `mapIds()` function has a `multiVals` argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. It is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. @@ -255,7 +255,8 @@ We are going to turn our list object into a data frame object in the next chunk. ```{r} # Let's make our object a bit more manageable for exploration by turning it into a data frame using the `reshape2::melt()` function mapped_df <- mapped_list %>% - reshape2::melt() + reshape2::melt(value.name = "Symbol") %>% + dplyr::rename("Ensembl" = "L1") # Here we are renaming the column holding the Ensembl IDs as good practice ``` Now let's take a peek at our data frame. @@ -264,12 +265,12 @@ Now let's take a peek at our data frame. head(mapped_df) ``` -We can see that our data frame has a new column `value`. -Let's get a summary of the gene symbols returned in the `value` column of our mapped data frame. +We can see that our data frame has a new column `Symbol`. +Let's get a summary of the gene symbols returned in the `Symbol` column of our mapped data frame. ```{r} -# We can use the `summary()` function to get a better idea of the distribution of symbols in the `value` column -summary(mapped_df$value) +# We can use the `summary()` function to get a better idea of the distribution of symbols in the `Symbol` column +summary(mapped_df$Symbol) ``` There are 998 NA's in our data frame, which means that 998 out of the 17918 Ensembl IDs did not map to gene symbols. @@ -282,8 +283,8 @@ Now let's check to see if we have any genes that were mapped to multiple symbols ```{r} multi_mapped <- mapped_df %>% - # Let's group by the Ensembl ID's in the `L1` column - dplyr::group_by(L1) %>% + # Let's group by the Ensembl IDs in the `Ensembl` column + dplyr::group_by(Ensembl) %>% # Create a new variable containing the number of symbols mapped to each ID dplyr::mutate(gene_symbol_count = dplyr::n()) %>% # Arrange by the genes with the highest number of symbols mapped @@ -297,47 +298,27 @@ head(multi_mapped) Looks like we have some cases where 3 gene symbols mapped to a single Ensembl ID. We have a total of 130 out of 17984 Ensembl IDs with multiple mappings to gene symbols. -We are not too worried about the 130 IDs with multiple mappings, so we will filter them out for the purpose of having 1:1 mappings for our downstream analysis. +If we are not too worried about the 130 IDs with multiple mappings, we can filter them out for the purpose of having 1:1 mappings for our downstream analysis. ## Map Ensembl IDs to gene symbols -- filtering out multi mappings -The next code chunk will rerun the `mapIds()` function, this time supplying the `"filter"` option to the `multiVals` argument. +The next code we chunk we will rerun the `mapIds()` function, this time supplying the `"filter"` option to the `multiVals` argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use `?mapIds` to see more options or strategies. ```{r} # Map ensembl IDs to their associated gene symbols -filtered_mapped_df <- mapIds( - org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data - keys = df$Gene, - column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to - keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data - multiVals = "filter" -) %>% - # Turn our list into a data frame - reshape2::melt() %>% - # Now let's give the `value` column a name that is more relevant to the values it holds - dplyr::rename("Symbol" = "value") -``` - -Let's copy the same code we used above to see if we have any multi mappings in our new data frame. - -```{r} -filtered_mapped_df %>% - # First, we need to grab the Ensembl IDs from the rownames - tibble::rownames_to_column("Gene") %>% - # Let's group by the Ensembl ID's in the `L1` column - dplyr::group_by(Gene) %>% - # Create a new variable containing the number of symbols mapped to each ID - dplyr::mutate(gene_symbol_count = dplyr::n()) %>% - # Arrange by the genes with the highest number of symbols mapped - dplyr::arrange(desc(gene_symbol_count)) %>% - # Filter to include only the rows with multi mappings - dplyr::filter(gene_symbol_count > 1) +filtered_mapped_df <- data.frame( + "Symbol" = mapIds( + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data + keys = df$Gene, + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "filter" + ) +) ``` -This new new `filtered_mapped_df` object is empty, as expected, since it has been filtered to include only 1:1 mappings. - Now, let's write our filtered and mapped results to file! ## Write mapped results to file @@ -354,8 +335,7 @@ readr::write_tsv(filtered_mapped_df, file.path( # Resources for further learning - Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020] -- [More on Annotation mapping functions](https://www.bioconductor.org/packages/release/bioc/vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf) [@Edwards2020] -- See our [RNA-seq gene ID conversion notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd) as another applicable example, since the steps for this workflow do not change with technology [@gene-id-annotation-rna-seq]. +- See our [RNA-seq gene ID conversion notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-annotation_rnaseq_01_ensembl.html) as another applicable example, since the steps for this workflow do not change with technology [@gene-id-annotation-rna-seq]. # Session info diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html index 99f25e4b..130aadcd 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.html +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html @@ -1625,7 +1625,7 @@
    • Introduction
      • -
    • Clustering and Heatmaps
      • +
    • Clustering and Heatmaps
    • Differential Expression
    • Dimension Reduction - PCA
    • Dimension Reduction - UMAP
      • @@ -1765,8 +1765,8 @@

      2.6 Check out our file structure!

      3 Using a different refine.bio dataset with this analysis?

      If you’d like to adapt an example analysis to use a different dataset from refine.bio, we recommend placing the files in the data/ directory you created and changing the filenames and paths in the notebook to match these files (we’ve put comments to signify where you would need to change the code). We suggest saving plots and results to plots/ and results/ directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences.

      -

      refine.bio data comes with gene level data with Ensembl IDs. Although this example script uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this script can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

      -

      For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. In the case of our RNA-seq gene identifier annotation example notebook, a Zebrafish (Danio rerio) dataset is used, meaning org.Dr.eg.db or Dr would also need to be used there. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).

      +

      refine.bio data comes with gene level data with Ensembl IDs. Although this example notebook uses Ensembl IDs from Mouse, (Mus musculus), to obtain gene symbols this notebook can be easily converted for use with different species or annotation types eg protein IDs, gene ontology, accession numbers.

      +

      For different species, wherever the abbreviation org.Mm.eg.db or Mm is written, it must be replaced with the respective species abbreviation eg for Homo sapiens org.Hs.eg.db or Hs would be used. In the case of our RNA-seq gene identifier annotation example notebook, a Zebrafish (Danio rerio) dataset is used, meaning org.Dr.eg.db or Dr would also need to be used there. A full list of the annotation R packages from Bioconductor is at this link (R Bioconductor Team 2003).

       

      @@ -1777,7 +1777,7 @@

      4 Obtaining Annotation for Ensemb

      4.1 Install libraries

      See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.

      -

      In this analysis, we will be using the org.Mm.eg.db R package (Carlson 2019).

      +

      In this analysis, we will be using the org.Mm.eg.db R package (Carlson 2019). Other species can be used.

      # Install the mouse package
 if (!("org.Mm.eg.db" %in% installed.packages())) {
   # Install this package if it isn't installed yet
@@ -1895,7 +1895,7 @@ 
      4.2 Import and set up data

      4.3 Map Ensembl IDs to gene symbols

      -

      This code chunk will return a message that indicates that there are many mappings between our keys and columns but this is okay and is what we expect in most cases since genes and transcripts do not have a 1:1 relationship. The mapIds() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. It is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use ?mapIds to see more options or strategies.

      +

      The mapIds() function has a multiVals argument which denotes what to do when there are multiple mapped values for a single gene identifier. The default behavior is to return just the first mapped value. It is good to keep in mind that various downstream analyses may benefit from varied strategies at this step. Use ?mapIds to see more options or strategies.

      In the next chunk, we will run the mapIds() function and supply the multiVals argument with the "list" option in order to get a large list with all the mapped values found for each gene identifier.

      # Map ensembl IDs to their associated gene symbols
 mapped_list <- mapIds(
@@ -1932,19 +1932,20 @@ 
      4.4 Explore gene ID conversionIt looks like we have gene symbols that were successfully mapped to the Ensembl IDs we provided. However, the data is now in a list object, making it a little more difficult to explore. We are going to turn our list object into a data frame object in the next chunk.
      
 
      # Let's make our object a bit more manageable for exploration by turning it into a data frame using the `reshape2::melt()` function
 mapped_df <- mapped_list %>%
-  reshape2::melt()
      
+  reshape2::melt(value.name = "Symbol") %>%
+  dplyr::rename("Ensembl" = "L1") # Here we are renaming the column holding the Ensembl IDs as good practice

      Now let’s take a peek at our data frame.

      head(mapped_df)
      -
      ##   value                 L1
-## 1 Gnai3 ENSMUSG00000000001
-## 2  Pbsn ENSMUSG00000000003
-## 3 Cdc45 ENSMUSG00000000028
-## 4   H19 ENSMUSG00000000031
-## 5 Scml2 ENSMUSG00000000037
-## 6  Apoh ENSMUSG00000000049
      -

      We can see that our data frame has a new column value. Let’s get a summary of the gene symbols returned in the value column of our mapped data frame.

      -
      # We can use the `summary()` function to get a better idea of the distribution of symbols in the `value` column
-summary(mapped_df$value)
      +
      ##   Symbol            Ensembl
+## 1  Gnai3 ENSMUSG00000000001
+## 2   Pbsn ENSMUSG00000000003
+## 3  Cdc45 ENSMUSG00000000028
+## 4    H19 ENSMUSG00000000031
+## 5  Scml2 ENSMUSG00000000037
+## 6   Apoh ENSMUSG00000000049
      +

      We can see that our data frame has a new column Symbol. Let’s get a summary of the gene symbols returned in the Symbol column of our mapped data frame.

      +
      # We can use the `summary()` function to get a better idea of the distribution of symbols in the `Symbol` column
+summary(mapped_df$Symbol)
      ##  Gm13023   Nudt10     Pms2    Gnai3     Pbsn    Cdc45      H19    Scml2 
 ##        2        2        2        1        1        1        1        1 
 ##     Apoh     Narf     Cav2     Klf6    Scmh1    Cox5a     Tbx2     Tbx4 
@@ -1975,8 +1976,8 @@ 
      4.4 Explore gene ID conversionHowever, if you have almost all NA’s it is possible that the function was executed incorrectly or you may want to consider using a different gene identifier, if possible.
      
 
      Now let’s check to see if we have any genes that were mapped to multiple symbols.
      
 
      multi_mapped <- mapped_df %>%
-  # Let's group by the Ensembl ID's in the `L1` column
-  dplyr::group_by(L1) %>%
+  # Let's group by the Ensembl IDs in the `Ensembl` column
+  dplyr::group_by(Ensembl) %>%
   # Create a new variable containing the number of symbols mapped to each ID
   dplyr::mutate(gene_symbol_count = dplyr::n()) %>%
   # Arrange by the genes with the highest number of symbols mapped
@@ -1987,8 +1988,8 @@ 
      4.4 Explore gene ID conversion
      
 
      ## # A tibble: 6 x 3
-## # Groups:   L1 [2]
-##   value        L1                 gene_symbol_count
+## # Groups:   Ensembl [2]
+##   Symbol       Ensembl            gene_symbol_count
 ##   <fct>        <chr>                          <int>
 ## 1 Rpl23        ENSMUSG00000071415                 3
 ## 2 LOC100044627 ENSMUSG00000071415                 3
@@ -1996,40 +1997,22 @@ 
      4.4 Explore gene ID conversion
      
-
      Looks like we have some cases where 3 gene symbols mapped to a single Ensembl ID. We have a total of 130 out of 17984 Ensembl IDs with multiple mappings to gene symbols. We are not too worried about the 130 IDs with multiple mappings, so we will filter them out for the purpose of having 1:1 mappings for our downstream analysis.
      
+
      Looks like we have some cases where 3 gene symbols mapped to a single Ensembl ID. We have a total of 130 out of 17984 Ensembl IDs with multiple mappings to gene symbols. If we are not too worried about the 130 IDs with multiple mappings, we can filter them out for the purpose of having 1:1 mappings for our downstream analysis.

      4.5 Map Ensembl IDs to gene symbols – filtering out multi mappings

      -

      The next code chunk will rerun the mapIds() function, this time supplying the "filter" option to the multiVals argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use ?mapIds to see more options or strategies.

      +

      The next code we chunk we will rerun the mapIds() function, this time supplying the "filter" option to the multiVals argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use ?mapIds to see more options or strategies.

      # Map ensembl IDs to their associated gene symbols
-filtered_mapped_df <- mapIds(
-  org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
-  keys = df$Gene,
-  column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
-  keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
-  multiVals = "filter"
-) %>%
-  # Turn our list into a data frame
-  reshape2::melt() %>%
-  # Now let's give the `value` column a name that is more relevant to the values it holds
-  dplyr::rename("Symbol" = "value")
      +filtered_mapped_df <- data.frame( + "Symbol" = mapIds( + org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data + keys = df$Gene, + column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "filter" + ) +)       
      ## 'select()' returned 1:many mapping between keys and columns
      -

      Let’s copy the same code we used above to see if we have any multi mappings in our new data frame.

      -
      filtered_mapped_df %>%
-  # First, we need to grab the Ensembl IDs from the rownames
-  tibble::rownames_to_column("Gene") %>%
-  # Let's group by the Ensembl ID's in the `L1` column
-  dplyr::group_by(Gene) %>%
-  # Create a new variable containing the number of symbols mapped to each ID
-  dplyr::mutate(gene_symbol_count = dplyr::n()) %>%
-  # Arrange by the genes with the highest number of symbols mapped
-  dplyr::arrange(desc(gene_symbol_count)) %>%
-  # Filter to include only the rows with multi mappings
-  dplyr::filter(gene_symbol_count > 1)
      -
      ## # A tibble: 0 x 3
-## # Groups:   Gene [0]
-## # … with 3 variables: Gene <chr>, Symbol <fct>, gene_symbol_count <int>
      -

      This new new filtered_mapped_df object is empty, as expected, since it has been filtered to include only 1:1 mappings.

      Now, let’s write our filtered and mapped results to file!

      @@ -2045,8 +2028,7 @@

      4.6 Write mapped results to file<

      5 Resources for further learning

      @@ -2097,9 +2079,6 @@

      6 Session info

      CCDL, 2020 Obtaining annotation for ensembl ids - rna-seq.

      -
      -

      Edwards S. M., 2020 Annotation mapping functions.

      -

      R Bioconductor Team, 2003 Packages found under annotationdata

      diff --git a/03-rnaseq/00-intro-to-rnaseq.html b/03-rnaseq/00-intro-to-rnaseq.html index e74f5560..6e947fbd 100644 --- a/03-rnaseq/00-intro-to-rnaseq.html +++ b/03-rnaseq/00-intro-to-rnaseq.html @@ -1625,7 +1625,7 @@

      diff --git a/references.bib b/references.bib index e02284e0..e489782e 100644 --- a/references.bib +++ b/references.bib @@ -49,13 +49,6 @@ @Manual{CCDL2020-cluster-validation url = {https://github.com/AlexsLemonade/training-modules/blob/3dbc6f3f53c680ec6aa2f513851c1cd4635cc31c/machine-learning/02-openpbta_consensus_clustering.Rmd#L310}, } -@Manual{Edwards2020, - title = {Annotation mapping functions}, - author = {Stefan McKinnon Edwards}, - year = {2020}, - url = {https://www.bioconductor.org/packages/release/bioc/vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf}, -} - @Website{Freytag2019, title = {Workshop: Dimension reduction with R}, author = {Saskia Freytag}, From 5e52faf7795092f64f46fd1f321f27a2b98b21a5 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 16 Sep 2020 20:19:42 -0400 Subject: [PATCH 14/18] fix typo and add step to join rest of data to final df --- .../gene-id-annotation_microarray_01_ensembl.Rmd | 8 ++++++-- .../gene-id-annotation_microarray_01_ensembl.html | 8 ++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index 1e43a2c2..10cb6250 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -302,7 +302,7 @@ If we are not too worried about the 130 IDs with multiple mappings, we can filte ## Map Ensembl IDs to gene symbols -- filtering out multi mappings -The next code we chunk we will rerun the `mapIds()` function, this time supplying the `"filter"` option to the `multiVals` argument. +The next code chunk we will rerun the `mapIds()` function, this time supplying the `"filter"` option to the `multiVals` argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use `?mapIds` to see more options or strategies. @@ -316,7 +316,11 @@ filtered_mapped_df <- data.frame( keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data multiVals = "filter" ) -) +) %>% + # Make an `Ensembl` column to store the rownames + tibble::rownames_to_column("Ensembl") %>% + # Join the remaining data from `df` using the Ensembl IDs + dplyr::inner_join(df, by = c("Ensembl" = "Gene")) ``` Now, let's write our filtered and mapped results to file! diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html index 130aadcd..325cc5b0 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.html +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html @@ -2001,7 +2001,7 @@

      4.4 Explore gene ID conversion

      4.5 Map Ensembl IDs to gene symbols – filtering out multi mappings

      -

      The next code we chunk we will rerun the mapIds() function, this time supplying the "filter" option to the multiVals argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use ?mapIds to see more options or strategies.

      +

      The next code chunk we will rerun the mapIds() function, this time supplying the "filter" option to the multiVals argument. This will remove all instances of multiple mappings and return a list of only the gene identifiers and symbols that had 1:1 mapping. Use ?mapIds to see more options or strategies.

      # Map ensembl IDs to their associated gene symbols
 filtered_mapped_df <- data.frame(
   "Symbol" = mapIds(
@@ -2011,7 +2011,11 @@ 
      4.5 Map Ensembl IDs to gene symbo
     keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
     multiVals = "filter"
   )
-)
      +) %>% + # Make an `Ensembl` column to store the rownames + tibble::rownames_to_column("Ensembl") %>% + # Join the remaining data from `df` using the Ensembl IDs + dplyr::inner_join(df, by = c("Ensembl" = "Gene"))       
      ## 'select()' returned 1:many mapping between keys and columns

      Now, let’s write our filtered and mapped results to file!

      From c843d2905e1de5cdee6e3dec498cf4c2d5dcbd4e Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 16 Sep 2020 20:35:17 -0400 Subject: [PATCH 15/18] fix link in reference file --- references.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/references.bib b/references.bib index e489782e..17f79552 100644 --- a/references.bib +++ b/references.bib @@ -60,7 +60,7 @@ @Manual{gene-id-annotation-rna-seq title = {Obtaining Annotation for Ensembl IDs - RNA-seq}, author = {CCDL}, year = {2020}, - url = {https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.Rmd}, + url = {https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-convert_rnaseq_01_ensembl.html}, } @Manual{Gonzalez2014, From 0fadca7cc62f17c39380b51e4271addf0749ca46 Mon Sep 17 00:00:00 2001 From: Chante Bethell <43576623+cbethell@users.noreply.github.com> Date: Thu, 17 Sep 2020 08:59:35 -0400 Subject: [PATCH 16/18] Apply @cansavvy's suggestions from code review Co-authored-by: Candace Savonen --- 02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index 10cb6250..dbfe80c3 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -241,7 +241,7 @@ mapped_list <- mapIds( ## Explore gene ID conversion -Now, let's take a look at our mapped object to see how the mapping went. +Now, let's take a look at our `mapped_list` to see how the mapping went. ```{r} # Let's use the `head()` function to take a preview at our mapped list @@ -314,7 +314,7 @@ filtered_mapped_df <- data.frame( keys = df$Gene, column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data - multiVals = "filter" + multiVals = "filter" # This will drop any `Gene`s that have multiple matches ) ) %>% # Make an `Ensembl` column to store the rownames From 62d29cb3860761efc53f0241a79c1f2cfe4070b2 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Thu, 17 Sep 2020 09:38:09 -0400 Subject: [PATCH 17/18] add reference for `AnnotationDbi` and rerun --- .../gene-id-annotation_microarray_01_ensembl.Rmd | 4 ++-- .../gene-id-annotation_microarray_01_ensembl.html | 15 +++++++++------ references.bib | 9 ++++++++- 3 files changed, 19 insertions(+), 9 deletions(-) diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd index dbfe80c3..0613c248 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.Rmd @@ -11,7 +11,7 @@ output: # Purpose of this analysis -The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using `AnnotationDbi` packages. +The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using `AnnotationDbi` packages [@Carlson2020-package]. ⬇️ [**Jump to the analysis code**](#analysis) ⬇️ @@ -338,7 +338,7 @@ readr::write_tsv(filtered_mapped_df, file.path( # Resources for further learning -- Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020] +- Marc Carlson has prepared a nice [Introduction to Bioconductor Annotation Packages](https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf) [@Carlson2020-vignette] - See our [RNA-seq gene ID conversion notebook](https://github.com/AlexsLemonade/refinebio-examples/blob/master/03-rnaseq/gene-id-annotation_rnaseq_01_ensembl.html) as another applicable example, since the steps for this workflow do not change with technology [@gene-id-annotation-rna-seq]. # Session info diff --git a/02-microarray/gene-id-annotation_microarray_01_ensembl.html b/02-microarray/gene-id-annotation_microarray_01_ensembl.html index 325cc5b0..ecc1dfbe 100644 --- a/02-microarray/gene-id-annotation_microarray_01_ensembl.html +++ b/02-microarray/gene-id-annotation_microarray_01_ensembl.html @@ -1666,7 +1666,7 @@

      September 2020

      1 Purpose of this analysis

      -

      The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using AnnotationDbi packages.

      +

      The purpose of this notebook is to provide an example of mapping gene IDs for microarray data obtained from refine.bio using AnnotationDbi packages (Carlson 2020a).

      ⬇️ Jump to the analysis code ⬇️

      @@ -1909,7 +1909,7 @@

      4.3 Map Ensembl IDs to gene symbo

      4.4 Explore gene ID conversion

      -

      Now, let’s take a look at our mapped object to see how the mapping went.

      +

      Now, let’s take a look at our mapped_list to see how the mapping went.

      # Let's use the `head()` function to take a preview at our mapped list
 head(mapped_list)
      ## $ENSMUSG00000000001
@@ -2009,7 +2009,7 @@ 
      4.5 Map Ensembl IDs to gene symbo
     keys = df$Gene,
     column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
     keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
-    multiVals = "filter"
+    multiVals = "filter" # This will drop any `Gene`s that have multiple matches
   )
 ) %>%
   # Make an `Ensembl` column to store the rownames
@@ -2031,7 +2031,7 @@ 
      4.6 Write mapped results to file<
 
      
 
      5 Resources for further learning
      
 
 
      
@@ -2077,8 +2077,11 @@ 
      6 Session info
      
 
      
 
      Carlson M., 2019 Genome wide annotation for mouse
      
 
      
-
      
-
      Carlson M., 2020 AnnotationDbi: Introduction to bioconductor annotation packages
      
+
      
+
      Carlson M., 2020a AnnotationDbi
      
+
      
+
      
+
      Carlson M., 2020b AnnotationDbi: Introduction to bioconductor annotation packages
      
 
      
 
      
 
      CCDL, 2020 Obtaining annotation for ensembl ids - rna-seq.
      
diff --git a/references.bib b/references.bib
index 17f79552..e4c9d515 100644
--- a/references.bib
+++ b/references.bib
@@ -28,13 +28,20 @@ @Website{Carlson2019
   url = {https://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html},
 }
 
-@Website{Carlson2020,
+@Website{Carlson2020-vignette,
   title = {AnnotationDbi: Introduction To Bioconductor Annotation Packages},
   author = {Marc Carlson},
   year = {2020},
   url = {https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf},
 }
 
+@Website{Carlson2020-package,
+  title = {AnnotationDbi},
+  author = {Marc Carlson},
+  year = {2020},
+  url = {https://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html},
+}
+
 @Manual{CCDL2020,
   title = {scRNA-seq Dimension Reduction},
   author = {CCDL},

From 1613e8fb67a12ffb761faaa16744c642bc928211 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Thu, 17 Sep 2020 09:52:53 -0400
Subject: [PATCH 18/18] fix order in `references.bib`

---
 references.bib | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/references.bib b/references.bib
index e4c9d515..f956d6b1 100644
--- a/references.bib
+++ b/references.bib
@@ -28,18 +28,18 @@ @Website{Carlson2019
   url = {https://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html},
 }
 
-@Website{Carlson2020-vignette,
-  title = {AnnotationDbi: Introduction To Bioconductor Annotation Packages},
+@Website{Carlson2020-package,
+  title = {AnnotationDbi},
   author = {Marc Carlson},
   year = {2020},
-  url = {https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf},
+  url = {https://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html},
 }
 
-@Website{Carlson2020-package,
-  title = {AnnotationDbi},
+@Website{Carlson2020-vignette,
+  title = {AnnotationDbi: Introduction To Bioconductor Annotation Packages},
   author = {Marc Carlson},
   year = {2020},
-  url = {https://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html},
+  url = {https://bioconductor.org/packages/release/bioc/vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf},
 }
 
 @Manual{CCDL2020,