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Objectives

Once we've obtained abundance counts for our genes/exons/transcripts, we are usually interested in identifying those genes/exons/transcripts that are differentially expressed. 

In this section, you will learn about  different tools for identifying differentially expressed genes from gene count data. The same data from the previous exercises will be used. The data contains 75 bp paired-end reads that have been generated in silico to replicate real gene count data from Drosophila. The data simulates two biological groups with three biological replicates per group (6 samples total). 

• Learn about DESeq2, DEXSeq and cuffdiff packages and the differences among these packages.
• Become familiar with basic R usage and installing Bioconductor modules.
• Learn how to use DESeq2 to identify differentially expressed genes.
• Learn how to use cuffdiff pacakge to identify differentially expressed genes.

Get set up

cds

cd my_rnaseq_course
cd day_3_partA/gene_expression_exercise 
# you should have already copied this over
 

Let's first start installing some R tools because they may take a while. 

ssh <username>@ls5.tacc.utexas.edu

idev -m 120 -q development -A UT-2015-05-18 -r CCBB_Day_3

Introduction

Most RNA-Seq experiments are conducted with the aim of identifying genes/exons that are differentially expressed between two or more conditions. Many computational tools are available for performing the statistical tests required to identify these genes/exons.

Simply put, all these tools do three steps (and they can vary in how they do these steps):

• Normalization of gene counts
  
  
• Represent the gene counts by a distribution that defines the relation between mean and variance (dispersion).
  
  
• Perform a statistical test for each gene to compare the distributions between conditions.
  
  
• Provide fold change,  P-value information, false discovery rate for each gene.

Why Normalize?

Normalization smooths out technical variations among the samples we are comparing so that we can more confidently attribute variations we see to biological reasons. 

We usually normalize for:

1. Sequencing depth: Say we are comparing gene counts in sample A against sample B.  If you start out with 10 million reads in sample A  vs 1 million reads in sample B, a 10 fold increase in expression in sample A is going to be purely due to its sequencing depth.
2. Gene length: A gene that is twice as long is likely to have twice as many reads sampling it.

From: Dillies A et al, A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis, 

Most commonly done normalization

RPKM: Normalizes for sequencing depth and gene length.

RPKM = reads per kilobase per million mapped reads

RPK= No.of Mapped reads/ length of transcript in kb (transcript length/1000)

RPKM = RPK/total no.of reads in million (total no of reads/ 1000000)

R and Bioconductor, very briefly...

R is a very common scripting language used in statistics. There are whole courses on using R going on in other SSI classrooms as we speak! Inside the R universe, you have access to an incredibly large number of useful statistical functions (Fisher's exact test, nonlinear least-squares fitting, ANOVA ...). R also has advanced functionality for producing plots and graphs as output. 

Regrettably, R is a bit of it's own bizarro world, as far as how its commands work. (Futhermore, Googling "R" to get help can be very frustrating.) The conventions of most other programming and scripting languages seem to have been re-invented by someone who wanted to do everything their own way in R. Just like we write shell scripts in bash, you can write R scripts that carry out complicated analyses.

module spider Rstats
module load Rstats
module load RstatsPackages/3.5.1

Hints for working with R

• Don't forget: it's q() to quit.
• For help with a function, type ?command. Try ?read.table. The q key gets you out of help, just like for a man page.
• The left arrow <- (less-than-dash) is the same as an equals sign =. You can use them interchangeably.
• The prompt we will sometimes be showing for R is >. Don't type this for a command. It is like the login1$ at the beginning of the bash prompt when you log in to Stampede2. It just means that you are in the R shell.

You can type the name of a variable to have its value displayed. Like this...

R
 
#Where are you in R- this is your working directory
getwd()

#Change working directory
setwd(mydirectory)
 
 x <- 10 + 5 + 6
 x
[1] 21

#Vectors- single data type
id <- c(1,2,3,4,5)
sex<- c("male","male","female","female","female")
id
sex
 
#Lists-multiple data types
x<-list(1:3, c("Dave", "Van", "Lesa"), c("male","male","female"))
x
str(x)
#Factors- predefined categorial variables
vfact <- factor(sex)
vfact

#Data frames - most tables get read into as data frames-lots of equal length vectors.
mydata <- data.frame(id,sex)
mydata
names(mydata) <- c("Person's ID","Person's sex")
mydata
#Functions to look at data structures
length(id)
typeof(id)
str(x)
summary(mydata)
class(mydata)
names(mydata)

#Reading data from files
read.table()
?read.table()
#Writing data into files
write.table()
 
#To see all the objects we have available
ls()
 
#to save all your objects and your command history within R
save.image(file="test.Rdata")
savehistory(file="test.Rhistory")
 
#when you reenter R, load already created objects and commands
load(file="test.Rdata")
loadhistory(file = "test.Rhistory")
 
#to quit out of R
q()
 
#to run a R script from the command line
R CMD BATCH <rscript>

Bioconductor packages for R

Like other languages, R can be expanded by loading packages. The R equivalent of Bioperl or Biopython is Bioconductor. Bioconductor can theoretically do things for you like convert sequences (none of us use it for that), but where it really shines is in doing statistical tests (where is it second-to-none in this list of languages). Many functions for analyzing microarray data are implemented in R, and this strength has now carried over to the analysis of RNAseq data.

Here's how you install two modules that we will need for this exercise: (But first, remember that to get R on stampede2, you'll need to load the R module or have a version of R installed locally)

#These commands will work for any Bioconductor package!
R
 
#within R
	source("https://bioconductor.org/biocLite.R")
	biocLite("BiocUpgrade")


#It will now prompt you if you want to update all packages. Say yes. It will ask you if you want to install R libraries into your personal/local R library. Say yes to this as well.
	
	biocLite("DESeq2")
	biocLite("readr")
	biocLite('tximport')
	biocLite('rhdf5')

When you start R later, you will not need to re-install the modules. You can load them with just these commands:

login1$ R

library('DESeq2')
library('readr')
library('tximport')
library('rhdf5')

#load libraries
library(tximport)
library(readr)
library("DESeq2")
library('rhdf5')

#Import a file called file_list with all the locations of the abundance.tsv files
#eg below:
#/stor/SCRATCH/sample1/abundances.tsv
#/stor/SCRATCH/sample2/abundances.tsv
#/stor/SCRATCH/sample3/abundances.tsv
#/stor/SCRATCH/sample4/abundances.tsv
files<-as.character(read.table("file_list", header=FALSE)$V1)
#Import a file called samples with the sample names corresponding to each file in the file_list
#eg below:
#sample1
#sample2
#sample3
#sample4
#look at the data structures
files

samples<-as.character(read.table("samples",header=FALSE)$V1)
names(files)<-samples
#look at the data structures
samples
files

#Import a file called sampletable which is a tab-delimited file that contains each samplename along with the condition
#eg below:
#samples        condition
#sample1        alc
#sample2        alc
#sample3        con
#sample4        con
sampleTable <-read.table("sampleTable",header=TRUE, row.names=1)
#look at the data structure
head(sampleTable)


#IMPORTANT: MAKE SURE THE SAMPLES AND FILE_LIST ARE IN THE SAME ORDER- SAMPLES SHOULD MATCH UP WITH FILES
samples==rownames(sampleTable) #should return TRUE for all


#Import a file called tx2gene.csv which a csv file that contains the transcript id to gene id mapping
#For Drosophila, this is located at: tx2gene.csv
tx2gene <- read.csv("tx2gene.csv")
#look at this data structure
#read in kallisto abundance files, summarizing by gene
txi <- tximport(files, type = "kallisto", tx2gene = tx2gene)
names(txi)


#make a deseq2 object from the kallisto summarized counts
ddsMatrix <- DESeqDataSetFromTximport(txi, sampleTable, ~condition)
ddsMatrix


#Optionally, if you want to save this count matrix as a file
write.csv(assay(ddsMatrix), file="genecounts.raw.csv")


#Optionally, if you want to do variance stabilizing transformation or regularized log transformation on this count matrix and then save as a file: This can become input to things like wgcna, pca
vsd<- vst(ddsMatrix)
write.csv(assay(vsd), file="genecounts.variancestabilized.csv")
 
#estimate size factors, dispersion, normalize and perform negative binomial test to compare across conditions
dds<-DESeq(ddsMatrix)
#collect results from the statistical testing, order by adjusted pvalue and write into an output file
res<-results(dds)
resOrdered <- res[order(res$padj),]
summary(res)
write.csv(resOrdered, "deseq2_kallisto_C1_vs_C2.csv")
 
#generate MA plot
pdf('MAPlot.pdf')
plotMA(dds,ylim=c(-2,2),main="DESeq2")
dev.off()



#save the R data and history
save.image(file="deseq2.kallisto.Rdata")
savehistory(file="deseq2.kallisto.Rhistory")

R CMD BATCH deseq2.kallisto.R

library("DESeq2")
 
#GET HTSEQ COUNTS AND SET UP SAMPLE TABLE
directory<-(getwd())
samples <- c("C1_count1.gff", "C1_count2.gff", "C1_count3.gff", "C2_count4.gff", "C2_count5.gff", "C2_count6.gff")
conditions <- c("control", "control", "control", "treated","treated","treated")
sampleTable<-data.frame(sampleName=samples, fileName=samples, condition=conditions)
sampleTable


#BUILD A DESEQ2 OBJECT FROM THE HTSEQ COUNTS DATA
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c("control", "treated"))
 
 
#LOOK AT THE DESEQ2 OBJECT WE'VE CREATED BY READING IN HTSEQ COUNT FILES
ddsHTSeq
 
#RUN THE STATISTICAL TEST IN ONE GO- NORMALIZATION, ESTIMATE DISPERSION/VARIANCE AND DO TEST FOR DIFFERENTIAL EXPRESSION 
dds<-DESeq(ddsHTSeq)
res<-results(dds)
res<-res[order(res$padj),]
mcols(res,use.names=TRUE)
summary(res)
 
#GENERATE MA PLOT
jpeg('MAPlot_htseq.jpg')
plotMA(dds,ylim=c(-2,2),main="DESeq2") 
dev.off()
 
#WRITE RESULTS INTO FILE
write.csv(as.data.frame(res),file="deseq2_htseq_C1_vs_C2.csv")

#IF YOU DIDNT WANT TO RUN THE SCRIPT INTERACTIVELY
R CMD BATCH deseq2.htseq.R

head results/deseq2_kallisto_C1_vs_C2.csv

#DESeq2 results
sed 's/,/\t/g' results/deseq2_kallisto_C1_vs_C2.csv|sort -n -r -k3,3|cut -f 1,3|head

 
#Notice the idiosyncracy with sort
sed 's/,/\t/g' results/deseq2_kallisto_C1_vs_C2.csv|sort -n -r -k3,3|grep -v 'e-0'|cut -f 1,3|head

#DESeq2 results

sed 's/,/\t/g' results/deseq2_kallisto_C1_vs_C2.csv|sort -n -k3,3|cut -f 1,3|head
 
#Notice the idiosyncracy with sort
sed 's/,/\t/g' results/deseq2_kallisto_C1_vs_C2.csv|sort -n -k3,3|grep -v 'e-0'|cut -f 1,3|head

#DESeq2 results
sed 's/,/\t/g' results/deseq2_kallisto_C1_vs_C2.csv|awk '{if ((($3>=1)||($3<=-1))&&($7<=0.05)) print $1,$3,$7}'|wc -l