I have successfully generated the simulation data using the function " makeExampleDESeqDataSet" of DESeq2,
But I have no idea to distinguish which genes are DEG and which are not.
I have successfully generated the simulation data using the function " makeExampleDESeqDataSet" of DESeq2,
But I have no idea to distinguish which genes are DEG and which are not.
have you followed the vignette?
You should always first check the help file for the function by typing a "?" and the function name:
?makeExampleDESeqDataSet ... Value: a ‘DESeqDataSet’ with true dispersion, intercept and beta values in the metadata columns. Note that the true betas are provided on the log2 scale. ...
So you have:
mcols(dds)$trueBeta
Note that this simulation is a very simple one, and the parameters are not based on any dataset. Better would be to estimate the mean and dispersion pairs from a dataset (as we did in the DESeq2 paper) and feed these vectors into the function (the input arguments can be vectors).
Thanks for the reply. I also got the $trueBeta. But the problem is how can I define DEGs or non-DEGs since it is just a vector of numbers. Usually, I thought the users can obtain the information about the DEG or non-DEGs of simulation data before evaluating the DE analysis methods.
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Yes, I even check the R code. The following is my data simulation code:
deseq2 <- makeExampleDESeqDataSet(n = 10000, m = 2 * 5, betaSD = 4, interceptMean = 4, interceptSD = 2, sizeFactors = rep, dispMeanRel = function(x) 4/x + 0.1)
Can U tell me the information about DEG and non-DEG in the deseq2?
Well you can run
but, by generating that dataset, you're at the equivalent stage of 1.3 in the users guide.