RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

DE Pathway Analysis

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RNA-seq_Flowchart4

In this section we will use the GAGE tool in R to test for significantly enriched sets of genes within those genes found to be significantly “up” and “down” in our UHR vs HBR differential gene expression analysis. Do we see enrichment for genes associated with brain related cell types and processes in the list of DE genes that have significant differential expression beween the UHR samples compared to the HBR samples?

What is gage?

The Generally Applicable Gene-set Enrichment tool (GAGE) is a popular bioconductor package used to perform gene-set enrichment and pathway analysis. The package works independent of sample sizes, experimental designs, assay platforms, and is applicable to both microarray and RNAseq data sets. In this section we will use GAGE and gene sets from the “Gene Ontology” (GO) and the MSigDB databases to perform pathway analysis.

First, launch R at the commandline, start RStudio, or launch a posit Cloud session:

R

Importing DE results for gage

Before we perform the pathway analysis we need to read in our differential expression results from the previous DE analysis.


#Define working dir paths
datadir = "/cloud/project/outdir"
#datadir = "~/workspace/rnaseq/de/htseq_counts/"

setwd(datadir)

#Load in the DE results file with only significant genes (http://genomedata.org/cri-workshop/deseq2/DE_sig_genes_DESeq2.tsv)
DE_genes <-read.table("DE_sig_genes_DESeq2.tsv", sep="\t", header=T, stringsAsFactors = F)

Now let’s go ahead and load GAGE and some other useful packages.

library(AnnotationDbi)
library(org.Hs.eg.db)
library(GO.db)
library(gage)

Setting up gene set databases

In order to perform our pathway analysis we need a list of pathways and their respective genes. There are many databases that contain collections of genes (or gene sets) that can be used to understand whether a set of mutated or differentially expressed genes are functionally related. Some of these resources include: GO, KEGG, MSigDB, and WikiPathways. For this exercise we are going to investigate GO and MSigDB. The GAGE package has a function for querying GO in real time: go.gsets(). This function takes a species as an argument and will return a list of gene sets and some helpful meta information for subsetting these lists. If you are unfamiliar with GO, it is helpful to know that GO terms are categorized into three gene ontologies: “Biological Process”, “Molecular Function”, and “Cellular Component”. This information will come in handy later in our exercise. GAGE does not provide a similar tool to investigate the gene sets available in MSigDB. Fortunately, MSigDB provides a download-able .gmt file for all gene sets. This format is easily read into GAGE using a function called readList(). If you check out MSigDB you will see that there are 8 unique gene set collections, each with slightly different features. For this exercise we will use the C8 - cell type signature gene sets collection, which is a collection of gene sets that contain cluster markers for cell types identified from single-cell sequencing studies of human tissue.

# Set up go database
go.hs <- go.gsets(species="human")
go.bp.gs <- go.hs$go.sets[go.hs$go.subs$BP]
go.mf.gs <- go.hs$go.sets[go.hs$go.subs$MF]
go.cc.gs <- go.hs$go.sets[go.hs$go.subs$CC]

# Here we will read in an MSigDB gene set that was selected for this exercise and saved to the course website. 
c8 <-"http://genomedata.org/rnaseq-tutorial/c8.all.v7.2.entrez.gmt"
all_cell_types <-readList(c8)

Annotating genes

OK, so we have our differentially expressed genes and we have our gene sets. However, if you look at one of the objects containing the gene sets you’ll notice that each gene set contains a series of integers. These integers are Entrez gene identifiers. But do we have comparable information in our DE gene list? Right now, no. Our previous results use Ensembl IDs as gene identifiers. We will need to convert our gene identifiers to the format used in the GO and MSigDB gene sets before we can perform the pathway analysis. Fortunately, Bioconductor maintains genome wide annotation data for many species, you can view these species with the OrgDb bioc view. This makes converting the gene identifiers relatively straightforward, below we use the mapIds() function to query the OrganismDb object for the Entrez id based on the Ensembl id. Because there might not be a one-to-one relationship with these identifiers we also use multiVals="first" to specify that only the first identifier should be returned. Another option would be to use multiVals="asNA" to ignore one-to-many mappings.

DE_genes$entrez <- mapIds(org.Hs.eg.db, keys=DE_genes$ensemblID, column="ENTREZID", keytype="ENSEMBL", multiVals="first")

Some clean-up and identifier mapping

After completing the annotation above you will notice that some of our Ensembl gene IDs were not mapped to an Entrez gene ID. Why did this happen? Well, this is actually a complicated point and gets at some nuanced concepts of how to define and annotate a gene. The short answer is that we are using two different resources that have annotated the human genome and there are some differences in how these resources have completed this task. Therefore, it is expected that there are some discrepencies. In the next few steps we will clean up what we can by first removing the ERCC spike-in genes and then will use a different identifier for futher mapping.

#Remove spike-in
DE_genes_clean <- DE_genes[!grepl("ERCC", DE_genes$ensemblID),]

##Just so we know what we have removed 
ERCC_gene_count <-nrow(DE_genes[grepl("ERCC", DE_genes$ensemblID),])
ERCC_gene_count

###Deal with genes that we do not have an Entrez ID for 
missing_ensembl_key<-DE_genes_clean[is.na(DE_genes_clean$entrez),]
DE_genes_clean <-DE_genes_clean[!DE_genes_clean$ensemblID %in% missing_ensembl_key$ensemblID,]

###Try mapping using a different key
missing_ensembl_key$entrez <- mapIds(org.Hs.eg.db, keys=missing_ensembl_key$Symbol, column="ENTREZID", keytype="SYMBOL", multiVal='first')

#Remove remaining genes 
missing_ensembl_key_update <- missing_ensembl_key[!is.na(missing_ensembl_key$entrez),]

#Create a Final Gene list of all genes where we were able to find an Entrez ID (using two approaches)
DE_genes_clean <-rbind(DE_genes_clean, missing_ensembl_key_update)

Final preparation of DESeq2 results for gage

OK, last step. Let’s format the differential expression results into a format suitable for the GAGE package. Basically this means obtaining the log2 fold change values and assigning entrez gene identifiers to these values.

# grab the log fold changes for everything
De_gene.fc <- DE_genes_clean$log2FoldChange

# set the name for each row to be the Entrez Gene ID
names(De_gene.fc) <- DE_genes_clean$entrez

Running pathway analysis

We can now use the gage() function to obtain the significantly perturbed pathways from our differential expression experiment.

Note on the abbreviations below: “bp” refers to biological process, “mf” refers to molecular function, and “cc” refers to cellular process. These are the three main categories of gene ontology terms/annotations that were mentioned above.

#Run GAGE
#go 
fc.go.bp.p <- gage(De_gene.fc, gsets = go.bp.gs)
fc.go.mf.p <- gage(De_gene.fc, gsets = go.mf.gs)
fc.go.cc.p <- gage(De_gene.fc, gsets = go.cc.gs)

#msigdb
fc.c8.p <- gage(De_gene.fc, gsets =all_cell_types)

###Convert to dataframes 
#Results for testing for GO terms which are up-regulated
fc.go.bp.p.up <- as.data.frame(fc.go.bp.p$greater)
fc.go.mf.p.up <- as.data.frame(fc.go.mf.p$greater)
fc.go.cc.p.up <- as.data.frame(fc.go.cc.p$greater)

#Results for testing for GO terms which are down-regulated
fc.go.bp.p.down <- as.data.frame(fc.go.bp.p$less)
fc.go.mf.p.down <- as.data.frame(fc.go.mf.p$less)
fc.go.cc.p.down <- as.data.frame(fc.go.cc.p$less)

#Results for testing for MSigDB C8 gene sets which are up-regulated
fc.c8.p.up <- as.data.frame(fc.c8.p$greater)

#Results for testing for MSigDB C8 gene sets which are down-regulated
fc.c8.p.down <- as.data.frame(fc.c8.p$less)

Explore significant results

Alright, now we have results with accompanying p-values (yay!).

What does “up-“ or “down-regulated” mean here, in the context of our UHR vs HBR comparison? It may help to open and review the data in your DE_genes_DESeq2.tsv file.

Look at the cellular process results from our GO analysis. Do the results match your expectation?


#Try doing something like this to find some significant results:
#View the top 20 significantly up- or down-regulated GO terms from the Cellular Component Ontology
head(fc.go.cc.p.up[order(fc.go.cc.p.up$p.val),], n=20)
head(fc.go.cc.p.down[order(fc.go.cc.p.down$p.val),], n=20)

#You can do the same thing with your results from MSigDB
head(fc.c8.p.up)
head(fc.c8.p.down)

More exploration

At this point, it will be helpful to move out of R and further explore our results locally. For the remainder of the exercise we are going to focus on the results from GO. We will use an online tool to visualize how the GO terms we uncovered are related to each other.

write.table(fc.go.cc.p.up, "fc.go.cc.p.up.tsv", quote = F, sep = "\t", col.names = T, row.names = T)
write.table(fc.go.cc.p.down, "fc.go.cc.p.down.tsv", quote = F, sep = "\t", col.names = T, row.names = T)
#quit(save="no")

Visualize

For this last step we will do a very brief introduction to visualizing our results. We will use a tool called GOView, which is part of the WEB-based Gene Set Ananlysis ToolKit (WebGestalt) suite of tools.

Step One

Step Two

Step Three

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