EdgeR - Differential Gene Expression Analysis using RNA-seq data - R Tutorial

#EdgeR​ #RNAseq​ #DEG #plot #scatterplot #LogFC​​ #R​ #Bioinformatics​ #Bigdata​ #Datascience #English #USA #England #UK

EdgeR is an R package used to analyze data from high-throughput sequencing assays such as RNA-Seq for differential gene expression.

Following steps should be followed if you want to reproduce the data for better understanding.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install(version = "3.12"

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install()

BiocManager::install ("edgeR")
library(edgeR)

setwd("D:/Post_PhD_Research/Expression Analysis/EdgeR")

load("mobData.RData")
head(mobData)
mobDataGroups (write angle bracket and “-” here) c("MM", "MM", "WM", "WM", "WW", "WW")

# MM="triple mutatnt shoot grafted onto triple mutant root"
# WM="wild-type shoot grafted onto triple mutant root"
# WW="wild-type shoot grafted onto wild-type root"

data
head(data)
d (write angle bracket and “-” here) DGEList(counts=mobData,group=factor(mobDataGroups))
d

Filtering the data
dim(d)

head(d$counts)
head(cpm(d))
apply(d$counts, 2, sum) # total gene counts per sample

keep (write angle bracket and “-” here) rowSums(cpm(d)(write “greater than” sign here)100) (write “greater than” sign here)= 2
d (write angle bracket and “-” here) d[keep,]
dim(d)
d$samples

Normalizing the data
d (write angle bracket and “-” here) calcNormFactors(d)
d

Data Exploration

Estimating the Dispersion
d1 (write angle bracket and “-” here) estimateCommonDisp(d, verbose=T)
names(d1)

d1 (write angle bracket and “-” here) estimateTagwiseDisp(d1)
names(d1)
plotBCV(d1)

GLM estimates of dispersion

plotBCV(d2)

Differential Expression
et12 (write angle bracket and “-” here) exactTest(d1, pair=c(1,2)) # compare groups 1 and 2
et13 (write angle bracket and “-” here) exactTest(d1, pair=c(1,3)) # compare groups 1 and 3
et23 (write angle bracket and “-” here) exactTest(d1, pair=c(2,3)) # compare groups 2 and 3
topTags(et12, n=10)
summary(de1)
abline(h = c(-2, 2), col = "blue")

[someone_there]

Seriously ? can no-one do a normal explanation ? Like talking with the mouth to explain things instead of showing text in a word file ? I really don't understand the R community... Any other language you can find plenty of good tutorials explaining everything in details. What is the problem of R ? please somebody do something about it. 😭

[scichores]

I wish there was sound instead of the music... Could you explain those plots?

[rereh-c]

Ohh.... I am a completly beginner and still clueless 😭😭😭😭😭

[HopeOverDebt]

Hi can you elaborate which dataset you have taken and where can i find the paper related to the data u have used

[hediatnani]

Hi. Why this music instead of the sound?

[waqarali]

The GEO dataset analyzed by high throughput sequencing don't have three three replicated in that case how we can calculate the P-Value and LogFc value ? Thanks in advance for your sincere cooperation

[johirislam]

I want have some other quaries regarding DEG analysis.I want to compare two datasets differentially expressed gene, how can i do that.For example one data set contain 108 DEG and the other contain 70 so i want to see the common gene between this two dataset.So how can i do that and how can i make the vaan diagram between them.Moreover i saw some GEO dataset there are some file format tsv and txt.Son in that case how can i analyse that kind of file.Plz solve this two problem to me.

[animeshpattnaik]

Hello sir, can we use only two data like one is treated and another is control for differential gene expression analysis in edge R. Because I have no replicates. If any suggestions ?

[kusumahosalli]

sir can pls you suggest algorithms works on micro array analysis?