Bioconductor Two-factorialdesign

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DESeq2 two-factorialdesign

updated 4.7 years ago by Michael Love 43k • written 4.8 years ago by dequattro.concetta ▴ 10

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Comment: Getting a large intercept with DESeq2 by Michael Love 43k

Thanks James -- Denise you can check the "note on factor levels" in the vignette. And I'd recommend `plotCounts()` for looking at individu…

Comment: Getting a large intercept with DESeq2 by James W. MacDonald 68k

As a trivial example, consider this: ``` > model.matrix(~Treatment, data.frame(Treatment = factor(rep(1:3, each = 4)))) (Intercept) Tre…

Answer: Getting a large intercept with DESeq2 by James W. MacDonald 68k

The default parameterization in R is a treatment-contrasts parameterization that sets one of your groups as the baseline. So the intercept …

Answer: Must I do pseudobulk analysis on Cell Surface Protein Labeling data of Single C by ATpoint ★ 5.0k

There is no "MUST", but pseudobulk aggregation is beneficial to a) enable use of tested and robust methods such as limma (though it could u…

Answer: Post translational modifications and phosphoproteomics in limpa? by Gordon Smyth 53k

Yes, we use limpa for PTMs ourselves. I assume your data is preprocessed so that each row corresponds to a PTM. You replace dpcQuant() with…

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