Polish Microarray GSEA example

02-microarray/pathway-analysis_microarray_01_ora.Rmd

@@ -132,7 +132,7 @@ From here you can customize this analysis example to fit your own scientific que
 
 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 [`clusterProfiler`](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) package to perform ORA and the [`msigdbr`](https://cran.r-project.org/web/packages/msigdbr/index.html) package which contains gene sets from the [Molecular Signatures Database (MSigDB)](https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) already in the tidy format required by `clusterProfiler` [@Igor2020; @Subramanian2005].
+In this analysis, we will be using [`clusterProfiler`](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) package to perform ORA and the [`msigdbr`](https://cran.r-project.org/web/packages/msigdbr/index.html) package which contains gene sets from the [Molecular Signatures Database (MSigDB)](https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) already in the tidy format required by `clusterProfiler` [@Yu2012; @Igor2020; @Subramanian2005; @Liberzon2011].
 
 We will also need the [`org.Dr.eg.db`](https://bioconductor.org/packages/release/data/annotation/html/org.Dr.eg.db.html) package to perform gene identifier conversion and [`ggupset`](https://cran.r-project.org/web/packages/ggupset/readme/README.html) to make an UpSet plot [@Carlson2019-zebrafish; @Ahlmann-Eltze2020].
 
@@ -239,7 +239,7 @@ dge_df
 
 ## Getting familiar with MSigDB gene sets available via `msigdbr`
 
-The Molecular Signatures Database (MSigDB) is a resource that contains annotated gene sets that can be used for pathway or gene set analyses [@Subramanian2005]. 
+The Molecular Signatures Database (MSigDB) is a resource that contains annotated gene sets that can be used for pathway or gene set analyses [@Subramanian2005; @Liberzon2011]. 
 We can use the `msigdbr` package to access these gene sets in a format compatible with the package we'll use for analysis, `clusterProfiler` [@Igor2020; @Yu2012].
 
 The gene sets available directly from MSigDB are applicable to human studies.
@@ -257,8 +257,8 @@ The data we're interested in here comes from zebrafish samples, so we can obtain
 dr_msigdb_df <- msigdbr(species = "Danio rerio")
 ```
 
-MSigDB contains [8 different gene set collections](https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp) [@Subramanian2005] that are distinguished by how they are derived (e.g., computationally mined, curated).
-In this example, we will use pathways that are gene sets considered to be "canonical representations of a biological process compiled by domain experts" and are a subset of `C2: curated gene sets` [@Subramanian2005].
+MSigDB contains [8 different gene set collections](https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp) [@Subramanian2005; @Liberzon2011] that are distinguished by how they are derived (e.g., computationally mined, curated).
+In this example, we will use pathways that are gene sets considered to be "canonical representations of a biological process compiled by domain experts" and are a subset of `C2: curated gene sets` [@Subramanian2005; @Liberzon2011].
 
 Specifically, we will use the [KEGG (Kyoto Encyclopedia of Genes and Genomes)](https://www.genome.jp/kegg/) pathways [@Kanehisa2000].
 
@@ -305,18 +305,20 @@ keytypes(org.Dr.eg.db)
 
 Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to gene symbols (`SYMBOL`), we could just as easily use it to convert from an Ensembl transcript ID (`ENSEMBLTRANS`) to Entrez IDs (`ENTREZID`).
 
-The function we will use to map from Ensembl gene IDs to gene symbols is called `mapIds()`.
+The function we will use to map from Ensembl gene IDs to gene symbols is called `mapIds()` and comes from the `AnnotationDbi` package.
 
 ```{r}
 # This returns a named vector which we can convert to a data frame, where
 # the keys (Ensembl IDs) are the names
-symbols_vector <- mapIds(org.Dr.eg.db, # Specify the annotation package
+symbols_vector <- mapIds(
+  # Replace with annotation package for the organism relevant to your data
+  org.Dr.eg.db,
   # The vector of gene identifiers we want to map
   keys = dge_df$Gene,
-  # The type of gene identifier we want returned
-  column = "SYMBOL",
-  # What type of gene identifiers we're starting with
+  # Replace with the type of gene identifiers in your data
   keytype = "ENSEMBL",
+  # Replace with the type of gene identifiers you would like to map to
+  column = "SYMBOL",
   # In the case of 1:many mappings, return the
   # first one. This is default behavior!
   multiVals = "first"
@@ -452,7 +454,7 @@ kegg_result_df <- data.frame(kegg_ora_results@result)
 
 Let's print out a sneak peek of it here and take a look at how many sets do we have that fit our cutoff of `0.1` FDR?
 
-```{r}
+```{r rownames.print = FALSE}
 kegg_result_df %>%
   dplyr::filter(p.adjust < 0.1)
 ```