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Hi everybody,
I just started using edgeR and DESeq and am looking for a confirmation that I am not doing a silly thing.
Basically, we have 3 conditions and for only 1 of these sample we have biological triplicates. Let us say that the samples are "A", "A", "A", "B" , "C" (most of the genes are NOT regulated in my experiment). Finally, let us say we just want to compare "B" to "C", but using all the information available. Can we use all the dataset for estimating the common and tagwise dispersion? Typically using the commands (note that I compare here "B" to "C", thus samples without replicates).
edgeR:
countTable=read.table('mytable',header=F,row.names=1) ; dge <- DGEList(counts=countTable,group=c("A","A","A","B","C")) ; dge <- calcNormFactors(dge) ; dge <- estimateCommonDisp(dge) ; dge <- estimateTagwiseDisp(dge) ; et <- exactTest(dge, pair=c("B","C"))
or
DESeq:
countTable = read.table('mytable.csv', header=F,row.names=1) ; design = data.frame(row.names = colnames(countTable),condition = c("A","A","A","B","C")) ; condition =design$condition ;cds=newCountDataSet(countTable,condition) ; cds=estimateSizeFactors(cds);cds=estimateDispersions(cds) res=nbinomTest(cds,"B","C")
Is it ok to do so (to use samples not compared in the end to estimate the dispersion) Does this correspond to the example "working partially without replicates" from the DESeq manual) ? Or should I just consider that there is no replicates for sample B and C and proceed by ignoring other samples completely ?
Many thanks !
Yvan
I just started using edgeR and DESeq and am looking for a confirmation that I am not doing a silly thing.
Basically, we have 3 conditions and for only 1 of these sample we have biological triplicates. Let us say that the samples are "A", "A", "A", "B" , "C" (most of the genes are NOT regulated in my experiment). Finally, let us say we just want to compare "B" to "C", but using all the information available. Can we use all the dataset for estimating the common and tagwise dispersion? Typically using the commands (note that I compare here "B" to "C", thus samples without replicates).
edgeR:
countTable=read.table('mytable',header=F,row.names=1) ; dge <- DGEList(counts=countTable,group=c("A","A","A","B","C")) ; dge <- calcNormFactors(dge) ; dge <- estimateCommonDisp(dge) ; dge <- estimateTagwiseDisp(dge) ; et <- exactTest(dge, pair=c("B","C"))
or
DESeq:
countTable = read.table('mytable.csv', header=F,row.names=1) ; design = data.frame(row.names = colnames(countTable),condition = c("A","A","A","B","C")) ; condition =design$condition ;cds=newCountDataSet(countTable,condition) ; cds=estimateSizeFactors(cds);cds=estimateDispersions(cds) res=nbinomTest(cds,"B","C")
Is it ok to do so (to use samples not compared in the end to estimate the dispersion) Does this correspond to the example "working partially without replicates" from the DESeq manual) ? Or should I just consider that there is no replicates for sample B and C and proceed by ignoring other samples completely ?
Many thanks !
Yvan