I am using DESeq2 for the analysis of my RNAseq data. I have the following sample set up:
A cell line inducibly expressing one of the 3 different proteins after DOX treatment. Luc2 = negative contol. ProteinA and ProteinB = proteins of interest.I am using the following model including an interaction term to correct for differences in the cells without DOX and the following results functions to extract the specific DEG for ProtA and ProtB:
design=~Condition + Treatment + Condition:Treatment dds <- DESeq(dds, betaPrior=FALSE, minReplicatesForReplace=Inf) res1 <- results(dds,name="ConditionProtA.TreatmentDOX", cooksCutoff=FALSE, independentFiltering=FALSE ) res2 <- results(dds,name="ConditionProtB.TreatmentDOX", cooksCutoff=FALSE, independentFiltering=FALSE )
I do have three replicates. The libraries from replicate1 were done on day1 and the libraries form replicates2+3 on day2.
If I do a PCA plot using rlog transformed counts I clearly see a batch effect --> best separation according to library prep Day1 and Day2.
Therefore I want to include a blocking factor in the design model.
Is it correct if I just change the design as follows?
~LibraryDay + Condition + Treatment + Condition:Treatment
Thanks for your help!