Too many DEG with edgeR output?
Hi there,
I'm dealing with bacterial RNA-seq analysis. My experiment is very simple. Two samples to compare and no replicates. Reads were generated with Ion Torrent PGM using 316 chip. One for each sample and performed in different days.
Since I had too many differentially expressed genes, ¿Should I be more conservative assigning edgeR dispersion value? Also, there are considerable more up-regulated genes in *exvivo* than in plate sample.
See logFC_vs_logCPM figure:
Thanks for you help, Bernardo
- tmap code:
tmap map2 -f HPNK_clean.fsa -r exvivo.fastq -i fastq -s exvivo.sam --verbose
tmap map2 -f HPNK_clean.fsa -r plate.fastq -i fastq -s plate.sam --verbose
- Flasgstat:
exvivo:
>3240242 + 0 in total (QC-passed reads + QC-failed reads)
>2132481 + 0 mapped (65.81%:nan%)
plate:
>3774075 + 0 in total (QC-passed reads + QC-failed reads)
>3510438 + 0 mapped (93.01%:nan%)
- count:
python -m HTSeq.scripts.count -m intersection-nonempty -t CDS -i ID exvivo.sam HPNK.gff > exvivo.counts
python -m HTSeq.scripts.count -m intersection-nonempty -t CDS -i ID plate.sam HPNK.gff > plate.counts
- count stats:
ex-vivo stats
>no_feature 777946
>ambiguous 1
>too_low_aQual 0
>not_aligned 1107761
>alignment_not_unique 0
plate stats
>no_feature 776707
>ambiguous 47
>too_low_aQual 0
>not_aligned 263637
>alignment_not_unique 0
- edgeR code:
library(edgeR)
files <- dir(pattern="*\\.counts$")
RG <- readDGE(files, header=FALSE)
RG
keep <- rowSums(cpm(RG)>1) >= 2 #we keep genes that achieve at least one count per million (cpm) in at least three samples
RG <- RG[keep,]
dim(RG)
RG <- calcNormFactors(RG)
RG$samples
plotMDS(RG)
bcv <- 0.2 #Assigned dispersion value of 0.2
m <- as.matrix(RG)
d <- DGEList(counts=m, group=(1:2)) #modify 'group' depending on sample number. Also can be adapted to replicated samples, see'?DGEList'.
d
et <- exactTest(d, pair=(1:2),dispersion=bcv^2) #exactTest(RG, pair=(1:2),dispersion=bcv^2)
et
top <- topTags(et)
top
cpm(RG)[rownames(top), ] #Check the individual cpm values for the top genes:
summary(de <- decideTestsDGE(et)) #The total number of DE genes at 5% FDR is given by'decideTestsDGE'.
[,1]
-1 200
0 1176
1 769
Of the 'number' tags identified as DE, 769 are up-regulated ex-vivo and 200 are down-regulated.
detags <- rownames(RG)[as.logical(de)] #detags contains the DE genes at 5% FDR
plotSmear(et, de.tags=detags) #plot all genes and highlight DE genes at 5% FDR
abline(h=c(-1, 1), col="blue") #The blue lines indicate 2-fold changes.
title("plate vs ex-vivo")
dev.copy2pdf(file = "Figure_1.pdf") #Save as .pdf##
I'm dealing with bacterial RNA-seq analysis. My experiment is very simple. Two samples to compare and no replicates. Reads were generated with Ion Torrent PGM using 316 chip. One for each sample and performed in different days.
Since I had too many differentially expressed genes, ¿Should I be more conservative assigning edgeR dispersion value? Also, there are considerable more up-regulated genes in *exvivo* than in plate sample.
See logFC_vs_logCPM figure:
Thanks for you help, Bernardo
- tmap code:
tmap map2 -f HPNK_clean.fsa -r exvivo.fastq -i fastq -s exvivo.sam --verbose
tmap map2 -f HPNK_clean.fsa -r plate.fastq -i fastq -s plate.sam --verbose
- Flasgstat:
exvivo:
>3240242 + 0 in total (QC-passed reads + QC-failed reads)
>2132481 + 0 mapped (65.81%:nan%)
plate:
>3774075 + 0 in total (QC-passed reads + QC-failed reads)
>3510438 + 0 mapped (93.01%:nan%)
- count:
python -m HTSeq.scripts.count -m intersection-nonempty -t CDS -i ID exvivo.sam HPNK.gff > exvivo.counts
python -m HTSeq.scripts.count -m intersection-nonempty -t CDS -i ID plate.sam HPNK.gff > plate.counts
- count stats:
ex-vivo stats
>no_feature 777946
>ambiguous 1
>too_low_aQual 0
>not_aligned 1107761
>alignment_not_unique 0
plate stats
>no_feature 776707
>ambiguous 47
>too_low_aQual 0
>not_aligned 263637
>alignment_not_unique 0
- edgeR code:
library(edgeR)
files <- dir(pattern="*\\.counts$")
RG <- readDGE(files, header=FALSE)
RG
keep <- rowSums(cpm(RG)>1) >= 2 #we keep genes that achieve at least one count per million (cpm) in at least three samples
RG <- RG[keep,]
dim(RG)
RG <- calcNormFactors(RG)
RG$samples
plotMDS(RG)
bcv <- 0.2 #Assigned dispersion value of 0.2
m <- as.matrix(RG)
d <- DGEList(counts=m, group=(1:2)) #modify 'group' depending on sample number. Also can be adapted to replicated samples, see'?DGEList'.
d
et <- exactTest(d, pair=(1:2),dispersion=bcv^2) #exactTest(RG, pair=(1:2),dispersion=bcv^2)
et
top <- topTags(et)
top
cpm(RG)[rownames(top), ] #Check the individual cpm values for the top genes:
summary(de <- decideTestsDGE(et)) #The total number of DE genes at 5% FDR is given by'decideTestsDGE'.
[,1]
-1 200
0 1176
1 769
Of the 'number' tags identified as DE, 769 are up-regulated ex-vivo and 200 are down-regulated.
detags <- rownames(RG)[as.logical(de)] #detags contains the DE genes at 5% FDR
plotSmear(et, de.tags=detags) #plot all genes and highlight DE genes at 5% FDR
abline(h=c(-1, 1), col="blue") #The blue lines indicate 2-fold changes.
title("plate vs ex-vivo")
dev.copy2pdf(file = "Figure_1.pdf") #Save as .pdf##