I have a peaks × experiments matrix of tag counts from ChIP-seq experiments in several conditions (with replicates), and I want to know which peaks are differentially enriched in different conditions. Basically, for each row of the matrix, I want to do an ANOVA (or something equivalent) asking if the enrichment is the same in all classes or not. Of course, I need to transform the raw counts in some way first.
Does anyone know how to do this rigorously? I know DESeq, edgeR, and DEGseq are designed for this kind of analysis, but as far as I can tell they only support a two-class comparison. Is there a way I can use them to compare more than two classes? Would it be appropriate just to use their variance stabilizations etc. to transform my data, then replace their two-class test with an ANOVA?
Also, each of my experiments has its own total chromatin control, so each control is a separate column in my matrix. Can these programs use ChIP-seq controls in some way, or are they only really for RNA-seq?
Does anyone know how to do this rigorously? I know DESeq, edgeR, and DEGseq are designed for this kind of analysis, but as far as I can tell they only support a two-class comparison. Is there a way I can use them to compare more than two classes? Would it be appropriate just to use their variance stabilizations etc. to transform my data, then replace their two-class test with an ANOVA?
Also, each of my experiments has its own total chromatin control, so each control is a separate column in my matrix. Can these programs use ChIP-seq controls in some way, or are they only really for RNA-seq?
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