Dear all,
I recently used DEseq to call DE genes and I have successfully run it for several dataset. However for the following dataset, I could not get any 'significant' DE gene with Padj < 0.05. Though, the within group variation between replicates are small and from the correlation between samples you can tell that CTR and Treated are obviously different. So my question is how could this happened? I have tried to figure out the problem but with no success.
Here is my R command for the analysis:
input file are attached behind. (ExperimentDesign.txt and RawCountTable.txt)
=============================
countTable.TBI.all = read.table("RawReadCount.txt",header=TRUE, row.names=1)
designTable.TBI.all = read.table("ExperimentDesign.txt",header=TRUE, row.names=1)
samples.CTR.vs.Treat = designTable.TBI.all$Condition == "CTR" | designTable.TBI.all$Condition =="Treat"
countTable.CTR.vs.Treat = countTable.TBI.all[ , samples.CTR.vs.Treat]
conditions.CTR.vs.Treat = designTable.TBI.all$Condition[samples.CTR.vs.Treat]
conditions.CTR.vs.Treat = factor(conditions.CTR.vs.Treat)
library(DESeq)
CTR.vs.Treat.cds = newCountDataSet(countTable.CTR.vs.Treat, conditions.CTR.vs.Treat)
CTR.vs.Treat.cds = estimateSizeFactors(CTR.vs.Treat.cds)
CTR.vs.Treat.cds = estimateDispersions(CTR.vs.Treat.cds)
CTR.vs.Treat.res = nbinomTest(CTR.vs.Treat.cds, "CTR", "Treat")
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I recently used DEseq to call DE genes and I have successfully run it for several dataset. However for the following dataset, I could not get any 'significant' DE gene with Padj < 0.05. Though, the within group variation between replicates are small and from the correlation between samples you can tell that CTR and Treated are obviously different. So my question is how could this happened? I have tried to figure out the problem but with no success.
Here is my R command for the analysis:
input file are attached behind. (ExperimentDesign.txt and RawCountTable.txt)
=============================
countTable.TBI.all = read.table("RawReadCount.txt",header=TRUE, row.names=1)
designTable.TBI.all = read.table("ExperimentDesign.txt",header=TRUE, row.names=1)
samples.CTR.vs.Treat = designTable.TBI.all$Condition == "CTR" | designTable.TBI.all$Condition =="Treat"
countTable.CTR.vs.Treat = countTable.TBI.all[ , samples.CTR.vs.Treat]
conditions.CTR.vs.Treat = designTable.TBI.all$Condition[samples.CTR.vs.Treat]
conditions.CTR.vs.Treat = factor(conditions.CTR.vs.Treat)
library(DESeq)
CTR.vs.Treat.cds = newCountDataSet(countTable.CTR.vs.Treat, conditions.CTR.vs.Treat)
CTR.vs.Treat.cds = estimateSizeFactors(CTR.vs.Treat.cds)
CTR.vs.Treat.cds = estimateDispersions(CTR.vs.Treat.cds)
CTR.vs.Treat.res = nbinomTest(CTR.vs.Treat.cds, "CTR", "Treat")
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