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  • DESeq: intepreting results from interaction analysis

    Hi there,

    I am using DESeq to infer differential gene expression in a 2-factor design. Factor 1 (strain) and Factor 2 (condition) each have 4 levels; 3 bioreps each totaling to 48 samples. I am interesed in identifying an interaction effect between strain and condition, on gene expression:

    > fit1 = fitNbinomGLMs( cdsFull, count ~ strain + condition + strain : condition )
    > fit0 = fitNbinomGLMs( cdsFull, count ~ strain + condition )
    > pvalsGLM = nbinomGLMTest( fit1, fit0 )
    > padjGLM = p.adjust( pvalsGLM, method='BH' )
    > DEresults <- transform( fit1, pval=pvalsGLM, padj=padjGLM )

    Attached are results for few significant genes. I am trying to understand the fold changes and p values.
    1. How do I identify which strain and which condition have a significant interaction, ie which comparison is the padj referring to? Do I need to do pairwise comparisons of each strain against each condition?

    2. If the Intercept represents foldchange ratio of strain1 : condition1, are the other columns subtracted differences in fold change?

    I can't seem to grasp the output, any tip is greatly appreciated.

    thanks
    Attached Files

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