[BioC] Interpreting DESeq2 results

[Michael Muratet]

Greetings

I have an experiment:

> design(dse)
~ factor1 + factor2 + factor3

where factor1 has two levels, factor2 has three levels and factor3 has three levels. I extract a gene of interest from the results for each term (I've changed the indices to reflect the condition):

> lapply(resultsNames(dse),function(u) results(dse,u)["gene_A",])
[["Intercept"]]
        baseMean log2FoldChange        pvalue           FDR
gene_A 1596.548       10.77485 3.309439e-216 7.025442e-216
[["factor1_level2"]]
        baseMean log2FoldChange    pvalue       FDR
gene_A 1596.548      0.3386776 0.1307309 0.3587438
[["factor2_level2"]]
        baseMean log2FoldChange    pvalue       FDR
gene_A 1596.548     -0.6882543 0.0613569 0.1007896
[["factor2_level3"]]
        baseMean log2FoldChange   pvalue       FDR
gene_A 1596.548      0.2393368 0.513216 0.6589575
[["factor3_level2"]]
        baseMean log2FoldChange    pvalue       FDR
gene_A 1596.548      0.1584153 0.6423634 0.8503163
[["factor3_level3]]
        baseMean log2FoldChange       pvalue         FDR
gene_A 1596.548      -1.627898 1.823141e-06 0.001409384

I want to be sure I understand the output format. Is it true that the coefficients (the vector beta) from the fit are the baseMean value scaled by the log2FoldChange? Is the true intercept value 1596.548*2^10.77485=2797274.13? 

mcols() tells me that the baseMean term is calculated over "all rows". The baseMean is different for different genes although it is the same for each gene across all the conditions, I'm not seeing how the rows are selected.

Thanks

Mike

Michael Muratet, Ph.D.
Senior Scientist
HudsonAlpha Institute for Biotechnology
mmuratet at hudsonalpha.org
(256) 327-0473 (p)
(256) 327-0966 (f)

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Huntsville, Alabama 35806