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:

https://docs.google.com/file/d/0B8-ZAuZe8jldY0Y5SWJSdXh1WGc/edit?usp=sharing

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##

-- 

*Bernardo Bello Ortí*

PhD student

CReSA-IRTA

Campus de Bellaterra-Universitat Autònoma de Barcelona

Edifici CReSA

08193  Bellaterra (Barcelona, Spain)

Tel.: 647 42 52 63 *www.cresa.es  *

*
*

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