Hello,

I want to generate a dummy sample data but I want to do it based on DESeq2 result.

Suppose, I have a gene, gene A and I have do DESeq2 analysis between cancer and normal. The result is, gene A logFold change is 2.5, which is upregulated in cancer with p-value 0.01. The baseMean is around 35.

What I want to do is to generate a dummy data for each category, cancer and normal which will have baseMean 35 and if it is caluclated back, it will give 2.5 log fold change with 0.01 p-value.

Is it possible to do that?

I imagine something like this. Because DESeq2 use negative binomial as distribution, I just need to make  random sampling with negative binomial distribution given the baseMean for normal 35 and for cancer 37.5 (35 + 2.5 fold Change). Is it possible? I don"t know though for the distribition variance. I don"t think it is written in the output of DESeq2 DEG analysis.

Thank you.

I can give some notes, but you're on your own for implementing this.

The fold change is multiplicative, so it would be 35 * 2.5.

Also note that the baseMean is not the level in normal samples, but it is the average over all samples. You can figure out the normalized counts for normal samples like so:

normMean <- rowMeans(counts(dds, normalized=TRUE)[, dds$condition == "normal"])

It is not easy to reproduce a p-value. Instead I would encourage you to focus on the dispersion values. You can pick mean, dispersion values and LFC from the observed distribution:

data <- cbind(norm=normMean, disp=dispersions(dds), lfc=res$log2FoldChange)