DESeq statistical test
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7.3 years ago
pmanga ▴ 60

 Hi,

I have this normalized dataset that I want to analyze the same way as DESeq analyzes a data set normalized within it for differential expression.Is there a way to get DESeq to analyze it for differential expression (DE)? Or if not (as I read that it needs raw read data and normalized data set cant be used) how can I use similar test of variance i.e. if I am not getting it wrong DESeq uses a chi square test can I replicate the same conditions(?) it uses elswhere to simulate a similar DE analysis as by DESeq?

RNA-Seq • 4.0k views
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How were the normalized numbers generated? You absolutely need raw counts to use DESeq2/edgeR/etc..

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the reads were normalized using the KDMM normalization in JMPgenomics. I was actually looking for comparing my results with the DESeq statistical test for this normalized data.

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Why not just use the raw counts fed into the KDMM algorithm? That would make more sense.

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If you already have normalized values for your data, you can use limma to do a differential gene expression test. For instance, when I have raw counts, instead of using the nbinomTest() function in DESeq to test for differential expression, I use variance stabilizing transformation (getVarianceStabilizedData() available in the DESeq package) to get normalized counts and then use limma to do a differential gene expression. So you can directly use limma on your normalized values.

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It'd be interesting to compare the results of that versus putting the normalized values through voom first. Since the variance stabilized values produced by DESeq2 aren't intended for statistical significance tests, I would be hesitant to just plug them into limma.

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Oh I did not know that. I referred to this paper (a long time back) and according to it, vst+limma & voom+limma can be used for differential tests and perform similarly. Since then, I have been using vst+limma for such tests. 

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I'd forgotten that paper. I'd have to compare the underlying methods to see what else voom is doing that vst isn't.

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