Hi! I have to perform a differential expression analysis for a RIP-seq experiment. In my design I have two variables treatment (miR and control) and Immunoprecipitation (Ago, IgG and TL). The sample table looks likes as follow:
samples treatment IP condition sample1 miR Ago Ago.miR sample2 miR Ago Ago.miR sample3 miR Ago Ago.miR sample4 miR IgG IgG.miR sample5 miR IgG IgG.miR sample6 miR IgG IgG.miR sample7 miR TL TL.miR sample8 miR TL TL.miR sample9 miR TL TL.miR sample10 Control Ago Ago.Control sample11 Control Ago Ago.Control sample12 Control Ago Ago.Control sample13 Control IgG IgG.Control sample14 Control IgG IgG.Control sample15 Control IgG IgG.Control sample16 Control TL TL.Control sample17 Control TL TL.Control sample18 Control TL TL.Control
I want to create a two-factorial design considering the variables treatment (miR and control) and Immunoprecipitation. In paticular I would to compare:
Ago miR vs Ago Control adjusting for the
IgG factor. The comparisons should looks like as follow:
(Ago.miR – IgG.miR) vs (Ago. control– IgG. control).
I was wondering if the right way to get this result is to create the dds model and extract the result as follow:
enter code here ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = samples, directory = directory, design= ~ treatment * IP) res <- results(dds, contrast=list(c("treatment_miR_vs_Control", "IP_Ago_vs_IgG")))