I have a data set from the following Design of Experiment(DOE)
sample batch condition FRT19A_se se FRT19A_Control_2 Hinfp_A_G_se se Hinfp_A_G_point_mutant Hinfp_M2_11_se se Hinfp_M2_11_Deletion_mutant W1118_se se W1118_Control_1 FRT19A_pe pe FRT19A_Control_2 Hinfp_A_G_pe pe Hinfp_A_G_point_mutant Hinfp_M2_11_pe pe Hinfp_M2_11_Deletion_mutant W1118_pe pe W1118_Control_1
We want to perform the following comparisons:
FRT19A vs Hinfp_A_G FRT19A vs Hinfp_M2_11 W1118 vs Hinfp_A_G W1118 vs Hinfp_M2_11 FRT19A vs W1118
Note: I have 4 samples, each has a replicate in batch(SE), another replicate in batch(PE)
My question is:
is this doable in DESeq2?
I did setting like
ddsFullCountTable<-DESeqDataSetFromMatrix( countData=dataCount, colData=DOE, design= ~batach + condition + batach:condition) dds <-DESeq(ddsFullCountTable)
estimating size factors estimating dispersions Error in checkForExperimentalReplicates(object, modelMatrix) : The design matrix has the same number of samples and coefficients to fit, so estimation of dispersion is not possible. Treating samples as replicates was deprecated in v1.20 and no longer supported since v1.22.
I think this does make sense, because, statistically, For this DOE, parameters are not estimable
Then I tried:
ddsFullCountTable<-DESeqDataSetFromMatrix( countData=rawdata.tli.after.filter.by.cpm, colData=DOE.2, design= ~batch+condition) re.se.pe.DOE.2 <- results(dds.DOE.2)
Then DESeq2 did get results !
However, I do not understand How DESeq2 calculate p value for this DOE?
Does anyone can help me on this?