RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Differential Expression


RNA-seq_Flowchart4


Ballgown DE Analyis

Use Ballgown to compare the tumor and normal conditions. Refer to the Ballgown manual for a more detailed explanation:

Change to ref-only directory:

mkdir -p $RNA_HOME/de/ballgown/ref_only/
cd $RNA_HOME/de/ballgown/ref_only/

Perform UHR vs. HBR comparison, using all replicates, for known (reference only mode) transcripts:

First create a file that lists our 6 expression files, then view that file, then start an R session where we will examine these results:

printf "\"ids\",\"type\",\"path\"\n\"UHR_Rep1\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep1\"\n\"UHR_Rep2\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep2\"\n\"UHR_Rep3\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep3\"\n\"HBR_Rep1\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep1\"\n\"HBR_Rep2\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep2\"\n\"HBR_Rep3\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep3\"\n" > UHR_vs_HBR.csv
cat UHR_vs_HBR.csv

R

A separate R tutorial file has been provided below. Run the R commands detailed in the R script.

###R code###

# load the required libraries
library(ballgown)
library(genefilter)
library(dplyr)
library(devtools)

# Load phenotype data from a file we saved in the current working directory
pheno_data = read.csv("UHR_vs_HBR.csv")

# Load ballgown data structure and save it to a variable "bg"
bg = ballgown(samples=as.vector(pheno_data$path), pData=pheno_data)

# Display a description of this object
bg

# Load all attributes including gene name
bg_table = texpr(bg, 'all')
bg_gene_names = unique(bg_table[, 9:10])

# Save the ballgown object to a file for later use
save(bg, file='bg.rda')

# Perform differential expression (DE) analysis with no filtering
results_transcripts = stattest(bg, feature="transcript", covariate="type", getFC=TRUE, meas="FPKM")
results_genes = stattest(bg, feature="gene", covariate="type", getFC=TRUE, meas="FPKM")
results_genes = merge(results_genes, bg_gene_names, by.x=c("id"), by.y=c("gene_id"))

# Save a tab delimited file for both the transcript and gene results
write.table(results_transcripts, "UHR_vs_HBR_transcript_results.tsv", sep="\t", quote=FALSE, row.names = FALSE)
write.table(results_genes, "UHR_vs_HBR_gene_results.tsv", sep="\t", quote=FALSE, row.names = FALSE)

# Filter low-abundance genes. Here we remove all transcripts with a variance across the samples of less than one
bg_filt = subset (bg,"rowVars(texpr(bg)) > 1", genomesubset=TRUE)

# Load all attributes including gene name
bg_filt_table = texpr(bg_filt , 'all')
bg_filt_gene_names = unique(bg_filt_table[, 9:10])

# Perform DE analysis now using the filtered data
results_transcripts = stattest(bg_filt, feature="transcript", covariate="type", getFC=TRUE, meas="FPKM")
results_genes = stattest(bg_filt, feature="gene", covariate="type", getFC=TRUE, meas="FPKM")
results_genes = merge(results_genes, bg_filt_gene_names, by.x=c("id"), by.y=c("gene_id"))

# Output the filtered list of genes and transcripts and save to tab delimited files
write.table(results_transcripts, "UHR_vs_HBR_transcript_results_filtered.tsv", sep="\t", quote=FALSE, row.names = FALSE)
write.table(results_genes, "UHR_vs_HBR_gene_results_filtered.tsv", sep="\t", quote=FALSE, row.names = FALSE)

# Identify the significant genes with p-value < 0.05
sig_transcripts = subset(results_transcripts, results_transcripts$pval<0.05)
sig_genes = subset(results_genes, results_genes$pval<0.05)

# Output the signifant gene results to a pair of tab delimited files
write.table(sig_transcripts, "UHR_vs_HBR_transcript_results_sig.tsv", sep="\t", quote=FALSE, row.names = FALSE)
write.table(sig_genes, "UHR_vs_HBR_gene_results_sig.tsv", sep="\t", quote=FALSE, row.names = FALSE)

# Exit the R session
quit(save="no")

Once you have completed the Ballgown analysis in R, exit the R session and continue with the steps below. A copy of the above R code is located here.

What does the raw output from Ballgown look like?

head UHR_vs_HBR_gene_results.tsv

How many genes are there on this chromosome?

grep -v feature UHR_vs_HBR_gene_results.tsv | wc -l

How many passed filter in UHR or HBR?

grep -v feature UHR_vs_HBR_gene_results_filtered.tsv | wc -l

How many differentially expressed genes were found on this chromosome (p-value < 0.05)?

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | wc -l

Display the top 20 DE genes. Look at some of those genes in IGV - do they make sense?

In the following commands we use grep -v feature to remove lines that contain “feature”. Then we use sort to sort the data in various ways. The k option specifies that we want to sort on a particular column (3 in this case which has the DE fold change values). The n option tells sort to sort numerically. The r option tells sort to reverse the sort.

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | sort -rnk 3 | head -n 20 | column -t #Higher abundance in UHR
grep -v feature UHR_vs_HBR_gene_results_sig.tsv | sort -nk 3 | head -n 20 | column -t #Higher abundance in HBR

Save all genes with P<0.05 to a new file.

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | cut -f 6 | sed 's/\"//g' > DE_genes.txt
head DE_genes.txt

ERCC DE Analysis

This section will compare the observed versus expected differential expression estimates for the ERCC spike-in RNAs:

cd $RNA_HOME/de/ballgown/ref_only
wget https://raw.githubusercontent.com/griffithlab/rnabio.org/master/assets/scripts/Tutorial_ERCC_DE.R
chmod +x Tutorial_ERCC_DE.R
./Tutorial_ERCC_DE.R $RNA_HOME/expression/htseq_counts/ERCC_Controls_Analysis.txt $RNA_HOME/de/ballgown/ref_only/UHR_vs_HBR_gene_results.tsv

View the results here:


edgeR Analysis

In this tutorial you will:

First, create a directory for results:

cd $RNA_HOME/
mkdir -p de/htseq_counts
cd de/htseq_counts

Note that the htseq-count results provide counts for each gene but uses only the Ensembl Gene ID (e.g. ENSG00000054611). This is not very convenient for biological interpretation. This next step creates a mapping file that will help us translate from ENSG IDs to Symbols. It does this by parsing the GTF transcriptome file we got from Ensembl. That file contains both gene names and IDs. Unfortunately, this file is a bit complex to parse. Furthermore, it contains the ERCC transcripts, and these have their own naming convention which also complicated the parsing.

perl -ne 'if ($_ =~ /gene_id\s\"(ENSG\S+)\"\;/) { $id = $1; $name = undef; if ($_ =~ /gene_name\s\"(\S+)"\;/) { $name = $1; }; }; if ($id && $name) {print "$id\t$name\n";} if ($_=~/gene_id\s\"(ERCC\S+)\"/){print "$1\t$1\n";}' $RNA_REF_GTF | sort | uniq > ENSG_ID2Name.txt
head ENSG_ID2Name.txt

Determine the number of unique Ensembl Gene IDs and symbols. What does this tell you?

#count unique gene ids
cut -f 1 ENSG_ID2Name.txt | sort | uniq | wc -l
#count unique gene names
cut -f 2 ENSG_ID2Name.txt | sort | uniq | wc -l

#show the most repeated gene names
cut -f 2 ENSG_ID2Name.txt | sort | uniq -c | sort -r | head

Launch R:

R

R code has been provided below. If you wish to have a script with all of the code, it can be found here. Run the R commands below.

###R code###
#Set working directory where output will go
working_dir = "~/workspace/rnaseq/de/htseq_counts"
setwd(working_dir)

#Read in gene mapping
mapping=read.table("~/workspace/rnaseq/de/htseq_counts/ENSG_ID2Name.txt", header=FALSE, stringsAsFactors=FALSE, row.names=1)

# Read in count matrix
rawdata=read.table("~/workspace/rnaseq/expression/htseq_counts/gene_read_counts_table_all_final.tsv", header=TRUE, stringsAsFactors=FALSE, row.names=1)

# Check dimensions
dim(rawdata)

# Require at least 25% of samples to have count > 25
quant <- apply(rawdata,1,quantile,0.75)
keep <- which((quant >= 25) == 1)
rawdata <- rawdata[keep,]
dim(rawdata)

#################
# Running edgeR #
#################

# load edgeR
library('edgeR')

# make class labels
class <- factor( c( rep("UHR",3), rep("HBR",3) ))

# Get common gene names
genes=rownames(rawdata)
gene_names=mapping[genes,1]


# Make DGEList object
y <- DGEList(counts=rawdata, genes=genes, group=class)
nrow(y)

# TMM Normalization
y <- calcNormFactors(y)

# Estimate dispersion
y <- estimateCommonDisp(y, verbose=TRUE)
y <- estimateTagwiseDisp(y)

# Differential expression test
et <- exactTest(y)

# Print top genes
topTags(et)

# Print number of up/down significant genes at FDR = 0.05  significance level
summary(de <- decideTestsDGE(et, p=.05))
detags <- rownames(y)[as.logical(de)]

# Output DE genes
# Matrix of significantly DE genes
mat <- cbind(
 genes,gene_names,
 sprintf('%0.3f',log10(et$table$PValue)),
 sprintf('%0.3f',et$table$logFC)
)[as.logical(de),]
colnames(mat) <- c("Gene", "Gene_Name", "Log10_Pvalue", "Log_fold_change")

# Order by log fold change
o <- order(et$table$logFC[as.logical(de)],decreasing=TRUE)
mat <- mat[o,]

# Save table
write.table(mat, file="DE_genes.txt", quote=FALSE, row.names=FALSE, sep="\t")

#To exit R type the following
quit(save="no")

Once you have run the edgeR tutorial, compare the sigDE genes to those saved earlier from cuffdiff:

cat $RNA_HOME/de/ballgown/ref_only/DE_genes.txt
cat $RNA_HOME/de/htseq_counts/DE_genes.txt

Pull out the gene IDs

cd $RNA_HOME/de/

cut -f 1 $RNA_HOME/de/ballgown/ref_only/DE_genes.txt | sort  > ballgown_DE_gene_symbols.txt
cut -f 2 $RNA_HOME/de/htseq_counts/DE_genes.txt | sort > htseq_counts_edgeR_DE_gene_symbols.txt

Visualize overlap with a venn diagram. This can be done with simple web tools like:

To get the two gene lists you could use cat to print out each list in your terminal and then copy/paste.

cat ballgown_DE_gene_symbols.txt
htseq_counts_edgeR_DE_gene_symbols.txt

Alternatively you could view both lists in a web browser as you have done with other files. These two files should be here: