## RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

# DE Pathway Analysis

In this secion we will use the GAGE tool in R to test for significantly enriched sets of genes within those genes found to be “up” and “down” in our HBR vs UHR 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 HBR samples compared to the UHR 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 MSigBDB databases to perform pathway analysis.

#Before we get started let's cd into the directory where our edgeR results are saved

cd ~/workspace/rnaseq/de/htseq_counts/


Launch R:

R


Let’s go ahead and load GAGE and some other useful packages and set our working directory.

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

setwd ("~/workspace/rnaseq/de/htseq_counts/")



### 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 go 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 the 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 c8, 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$CC] # Here we will read in an MSigDB gene set that was selected for this exercise and saved to the course website. c8 <-"http://genomedata.org/rnaseq-tutorial/c8.all.v7.2.entrez.gmt" all_cell_types <-readList(c8)  ### Importing edgeR results for gage Before we perform the pathway analysis we need to read in our differential expression results from the edgeR analysis. DE_genes <-read.table("/home/ubuntu/workspace/rnaseq/de/htseq_counts/DE_genes.txt",sep="\t",header=T,stringsAsFactors = F)  ### Annotating genes OK, so we have our differentially expressed genes and we have our gene sets. However, if you look at one of the objects containing the gene sets you’ll notice that each gene set contains a series of integers. These integers are Entrez gene identifiers. But do we have comparable information in our DE gene list? Right now, no. Our previous results use Ensembl IDs as gene identifiers. We will need to convert our gene identifiers to the format used in the GO and MSigDB gene sets before we can perform the pathway analysis. Fortunately, Bioconductor maintains genome wide annotation data for many species, you can view these species with the OrgDb bioc view. This makes converting the gene identifiers relatively straightforward, below we use the mapIds() function to query the OrganismDb object for the Entrez id based on the Ensembl id. Because there might not be a one-to-one relationship with these identifiers we also use multiVals="first" to specify that only the first identifier should be returned. DE_genes$entrez <- mapIds(org.Hs.eg.db, keys=DE_genes$Gene, column="ENTREZID", keytype="ENSEMBL", multiVals="first")  ### Some clean-up and identifier mapping After completing the annotation above you will notice that some of our Ensembl gene IDs were not mapped to an Entrez gene ID. Why did this happen? Well, this is actually a complicated point and gets at some nuanced concepts of how to define and annotate a gene. The short answer is that we are using two different resources that have annotated the human genome and there are some differences in how these resources have completed this task. Therefore, it is expected that there are some discrepencies. In the next few steps we will clean up what we can by first removing the ERCC spike-in genes and then will use a different identifier for futher mapping. #Remove spike-in DE_genes_clean <- DE_genes[!grepl("ERCC",DE_genes$Gene),]

##Just so we know what we have removed
ERCC_gene_count <-nrow(DE_genes[grepl("ERCC",DE_genes$Gene),]) ERCC_gene_count ###Deal with genes that we do not have an Entrez ID for #missing_ensembl_key<-DE_genes_clean[is.na(DE_genes_clean$entrez),]
#DE_genes_clean <-DE_genes_clean[!DE_genes_clean$Gene_Name %in% missing_ensembl_key$Gene_Name,]

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

#Remove remaining genes
#missing_ensembl_key_update <- missing_ensembl_key[!is.na(missing_ensembl_key$entrez),] #Create a Final Gene list of all genes where we were able to find an Entrez ID (using two approaches) #DE_genes_clean <-rbind(DE_genes_clean,missing_ensembl_key_update)  ### Final preparation of edgeR results for gage OK, last step. Let’s format the differential expression results into a format suitable for the GAGE package. Basically this means obtaining the log2 fold change values and assigning entrez gene identifiers to these values. # grab the log fold changes for everything De_gene.fc <- DE_genes_clean$logFC

# set the name for each row to be the Entrez Gene ID
names(De_gene.fc) <- DE_genes_clean$entrez  ### Running pathway analysis We can now use the gage() function to obtain the significantly perturbed pathways from our differential expression experiment. Note on the abbreviations below: “bp” refers to biological process, “mf” refers to molecular function, and “cc” refers to cellular process. These are the three main categories of gene ontology terms/annotations that were mentioned above. #Run GAGE #go fc.go.bp.p <- gage(De_gene.fc, gsets = go.bp.gs) fc.go.mf.p <- gage(De_gene.fc, gsets = go.mf.gs) fc.go.cc.p <- gage(De_gene.fc, gsets = go.cc.gs) #msigdb fc.go.c8.p <- gage(De_gene.fc, gsets =all_cell_types) ###Convert to dataframes fc.go.bp.p.up <- as.data.frame(fc.go.bp.p$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)

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$less) fc.go.c8.p.up <- as.data.frame(fc.go.c8.p$greater)
fc.go.c8.p.down <- as.data.frame(fc.go.c8.p$less)  ### Explore significant results Alright, now we have results with accompanying p-values (yay!).  #At this point we will give you a hint. Look at the cellular process results. Try doing something like this to find some significant results: head(fc.go.cc.p.down[order(fc.go.cc.p.down$p.val),], n=20)

#Do this with more of your results files to see what else you have uncovered

#You can do the same thing with your results from MSigDB



### 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,"/home/ubuntu/workspace/rnaseq/de/htseq_counts/fc.go.cc.p.up.tsv",quote = F,sep = "\t",col.names = T,row.names = T)
write.table(fc.go.cc.p.down,"/home/ubuntu/workspace/rnaseq/de/htseq_counts/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

• Download the linked files by right clicking on the two saved result files: fc.go.cc.p.up.tsv and fc.go.cc.p.down.tsv.

• Open the result file in your text editor of choice. We like text wrangler. You should also be able to open the file in excel, google sheets, or another spreadsheet tool. This might help you visualize the data in rows and columns (NB: There might be a small amount of formatting necessary to get the header to line up properly).

• You can either create an input file using this file as a guide, or you can simply use your downloaded data to cut and paste your GO terms of interest directly into GOView

*Step Two]

Step Three

• Navigate to the GOView tool

• Then, input the GO terms you would like to explore into the GOView interface by following the steps described in the “Beginning an analysis” section of the webpage. We will walk through a sample analysis.

• Explore the outputs!