[BioC] DESeq and EdgeR log fold differences
Ioannis Vlachos
iv at on.gr
Thu Nov 29 23:06:17 CET 2012
Hello everyone,
I thought of conducting a parallel DE analysis with EdgeR and DESeq using a
dataset that I have been working on lately.
The dataset has two conditions with two biological replicates each.
Let's say: Wild Type, Wild Type, Knock Out, Knock Out.
It's a smallRNA-Seq dataset, mapped to miRNAs.
I have tried various analyses using both programs and I have noticed this.
There are very large differences in fold changes for some miRNAs between the
two programs, even when using "RLE" for EdgeR normalization.
Example:
DESeq Code:
countDataSet = newCountDataSet (DATA, condition)
countDataSet = estimateSizeFactors(countDataSet)
countDataSet = estimateDispersions(countDataSet)
difexp = nbinomTest (countDataSet, "WildType", "KnockOut")
one of the results is:
id baseMean baseMeanA baseMeanB foldChange
log2FoldChange pval padj
100 623.8597966 349.3576527 898.3619406 2.57146776
1.362592066 0.001303353 0.182310802
And the size factors for DESeq are:
sizeFactors(countDataSet)
KO1 KO2 WT1 WT2
1.2969960 1.052 0.8850 0.84442
OK. So far so good.
EdgeR now.
dge <- DGEList(counts=DATA, group=condition)
dge<- calcNormFactors(dge)
dge <- estimateCommonDisp(dge, verbose=TRUE)
dge <-estimateTagwiseDisp(dge, verbose=TRUE)
et<- exactTest(dge)
Which results in:
logFC logCPM PValue FDR
100 -2.750 5.814103 9.40E-09 1.20E-05
With:
dge$samples
group lib.size norm.factors
WT1 1 2796302 0.9922204
WT2 1 2610244 0.9928183
KO1 2 3999488 1.0248098
KO2 2 3349646 0.9905555
We have logFC 1.3 for DESeq and 2.75 in EdgeR
And these results remain practically the same even by using:
dge<- calcNormFactors(dge.RLE, method="RLE")
logFC logCPM PValue FDR
210 -2.775952 5.823856 8.047631e-09 1.030902e-05
group lib.size norm.factors
KnockOut 3999488 1.0147156
KnockOut 3349646 0.9830163
WildType 2796302 0.9903844
WildType 2610244 1.0122579
Any thoughts?
This entry has (raw tags):
KO KO WT WT
131 123 287 195
Any thoughts on why I get 1.3 lFC vs 2.7lFC?
Thanks a lot,
Best Regards,
Ioannis
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