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RNA-Seq Header Section #216

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Sep 18, 2020
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RNA-Seq Header Section #216

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bf8d000
Get it started
cansavvy Sep 10, 2020
c278547
I put words down
cansavvy Sep 10, 2020
3d1ddef
moar words and links
cansavvy Sep 10, 2020
8e31eed
More words and citations
cansavvy Sep 15, 2020
29b73ac
RNA-seq header section a bit more polished
cansavvy Sep 15, 2020
a0663af
add figure in
cansavvy Sep 15, 2020
2fa1990
Few tiny edits
cansavvy Sep 15, 2020
19fb0ac
Incorporate @cbethell review
cansavvy Sep 16, 2020
e86cb52
Fix one little wording change
cansavvy Sep 16, 2020
a63f03e
Put a TODO for that one link
cansavvy Sep 17, 2020
7e3950a
Incorporate most of the comments in Jackie's review
cansavvy Sep 17, 2020
493f749
Re-render
cansavvy Sep 17, 2020
c3e058e
Re-render after fixing references.bib
cansavvy Sep 17, 2020
5ed3afd
More wording changes
cansavvy Sep 17, 2020
c93c3c9
Doctoc and re-render
cansavvy Sep 17, 2020
08eae92
rearrange wording about normalization
cansavvy Sep 17, 2020
f024ffd
Re-render
cansavvy Sep 17, 2020
4778a09
A few more minor edits
cansavvy Sep 17, 2020
bf9a12e
Just a few more wording edits and sentence rearrangments
cansavvy Sep 17, 2020
be1161b
Merge branch 'master' into cansavvy/rna-seq-header
cansavvy Sep 17, 2020
8904ffc
Merge origin/master into cansavvy/rna-seq-header
jaclyn-taroni Sep 18, 2020
779779d
Alphabetical order after resolving conflicts
jaclyn-taroni Sep 18, 2020
ddfcef8
Merge branch 'master' into cansavvy/rna-seq-header
jaclyn-taroni Sep 18, 2020
5a0fb1c
Few smaller changes and rerender everything
cansavvy Sep 18, 2020
56e77e1
Add links to the RNA-seq header section
cansavvy Sep 18, 2020
ab0c39a
Merge branch 'master' into cansavvy/rna-seq-header
cansavvy Sep 18, 2020
f176ef0
Get rid of the one typo Jackie found
cansavvy Sep 18, 2020
bea4e3f
Merge remote-tracking branch 'origin/cansavvy/rna-seq-header' into ca…
cansavvy Sep 18, 2020
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13 changes: 9 additions & 4 deletions 01-getting-started/getting-started.html
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13 changes: 9 additions & 4 deletions 02-microarray/00-intro-to-microarray.html
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<meta name="generator" content="pandoc" />
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66 changes: 38 additions & 28 deletions 02-microarray/clustering_microarray_01_heatmap.html
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103 changes: 48 additions & 55 deletions 02-microarray/gene-id-annotation_microarray_01_ensembl.html
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<meta name="generator" content="pandoc" />
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Expand Down Expand Up @@ -1783,6 +1788,8 @@ <h2><span class="header-section-number">4.1</span> Install libraries</h2>
# Install this package if it isn&#39;t installed yet
BiocManager::install(&quot;org.Mm.eg.db&quot;, update = FALSE)
}</code></pre>
<pre><code>## Bioconductor version 3.11 (BiocManager 1.30.10), R 4.0.2 (2020-06-22)</code></pre>
<pre><code>## Installing package(s) &#39;org.Mm.eg.db&#39;</code></pre>
<p>Attach the packages we need for this analysis.</p>
<pre class="r"><code># Attach the library
library(org.Mm.eg.db)</code></pre>
Expand Down Expand Up @@ -1884,9 +1891,10 @@ <h2><span class="header-section-number">4.2</span> Import and set up data</h2>
<p>Let’s ensure that the metadata and data are in the same sample order.</p>
<pre class="r"><code># Make the data in the order of the metadata
df &lt;- df %&gt;%
dplyr::select(metadata$geo_accession)

# Check if this is in the same order
dplyr::select(metadata$geo_accession)</code></pre>
<pre><code>## Warning: replacing previous import &#39;vctrs::data_frame&#39; by &#39;tibble::data_frame&#39;
## when loading &#39;dplyr&#39;</code></pre>
<pre class="r"><code># Check if this is in the same order
all.equal(colnames(df), metadata$geo_accession)</code></pre>
<pre><code>## [1] TRUE</code></pre>
<pre class="r"><code># Bring back the &quot;Gene&quot; column in preparation for mapping
Expand Down Expand Up @@ -1946,32 +1954,8 @@ <h2><span class="header-section-number">4.4</span> Explore gene ID conversion</h
<p>We can see that our data frame has a new column <code>Symbol</code>. Let’s get a summary of the gene symbols returned in the <code>Symbol</code> column of our mapped data frame.</p>
<pre class="r"><code># We can use the `summary()` function to get a better idea of the distribution of symbols in the `Symbol` column
summary(mapped_df$Symbol)</code></pre>
<pre><code>## 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&#39;s
## 1 1 16885 998</code></pre>
<pre><code>## Length Class Mode
## 17977 character character</code></pre>
<p>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.</p>
<p>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.</p>
<p>Now let’s check to see if we have any genes that were mapped to multiple symbols.</p>
Expand All @@ -1990,7 +1974,7 @@ <h2><span class="header-section-number">4.4</span> Explore gene ID conversion</h
<pre><code>## # A tibble: 6 x 3
## # Groups: Ensembl [2]
## Symbol Ensembl gene_symbol_count
## &lt;fct&gt; &lt;chr&gt; &lt;int&gt;
## &lt;chr&gt; &lt;chr&gt; &lt;int&gt;
## 1 Rpl23 ENSMUSG00000071415 3
## 2 LOC100044627 ENSMUSG00000071415 3
## 3 LOC100862455 ENSMUSG00000071415 3
Expand Down Expand Up @@ -2040,39 +2024,48 @@ <h1><span class="header-section-number">6</span> Session info</h1>
<p>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.</p>
<pre class="r"><code># Print session info
sessionInfo()</code></pre>
<pre><code>## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04 LTS
##
## 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
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-openmp/libopenblasp-r0.3.8.so
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## [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
##
## 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
## [1] magrittr_1.5 org.Mm.eg.db_3.11.4 AnnotationDbi_1.50.3
## [4] IRanges_2.22.2 S4Vectors_0.26.1 Biobase_2.48.0
## [7] BiocGenerics_0.34.0 optparse_1.6.6
##
## loaded via a namespace (and not attached):
## [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</code></pre>
## [1] Rcpp_1.0.5 plyr_1.8.6 pillar_1.4.6
## [4] compiler_4.0.2 BiocManager_1.30.10 R.methodsS3_1.8.1
## [7] R.utils_2.10.1 tools_4.0.2 bit_1.1-15.2
## [10] digest_0.6.25 memoise_1.1.0 RSQLite_2.2.0
## [13] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.3
## [16] R.cache_0.14.0 pkgconfig_2.0.3 rlang_0.4.7
## [19] DBI_1.1.0 cli_2.0.2 rstudioapi_0.11
## [22] yaml_2.2.1 xfun_0.17 dplyr_1.0.0
## [25] styler_1.3.2 stringr_1.4.0 knitr_1.29
## [28] generics_0.0.2 vctrs_0.3.4 hms_0.5.3
## [31] tidyselect_1.1.0 bit64_0.9-7.1 getopt_1.20.3
## [34] glue_1.4.2 R6_2.4.1 fansi_0.4.1
## [37] rmarkdown_2.3 reshape2_1.4.4 blob_1.2.1
## [40] purrr_0.3.4 readr_1.3.1 rematch2_2.1.2
## [43] backports_1.1.9 ellipsis_0.3.1 htmltools_0.5.0
## [46] assertthat_0.2.1 utf8_1.1.4 stringi_1.5.3
## [49] crayon_1.3.4 R.oo_1.24.0</code></pre>
<div id="refs" class="references">
<div id="ref-Carlson2019">
<p>Carlson M., 2019 Genome wide annotation for mouse</p>
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