Log-cpm values from limma
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@14ef1b09
Last seen 2 hours ago
Egypt

I have a performed a typical differential expression analysis using limma voom and I want to extract the log cpm values to draw ROC curves. But I want to extract the log cpm values produced specifically by limma because they are normalised and free of batch effect that was added to the design matrix. Is there a function to do that?

Thanks in advance

limma edgeR RNASeq • 330 views
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@gordon-smyth
Last seen 5 hours ago
WEHI, Melbourne, Australia

The log-cpm values from voom() are not free of batch effect. If you want batch-corrected log-cpm values, we recommend cpm() followed by removeBatchEffect().

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I have performed RUVg correction and extracted W, factors of unwanted variation. According to limma guide,

In the limma approach to RNA-seq, read counts are converted to log2-counts-per-million (logCPM) and the mean-variance relationship is modeled either with precision weights or with an empirical Bayes prior trend.

design <- model.matrix(~0+dge$samples$Stage + W)

Does this mean if I extract the logcpm counts using

   v=voom(dge, design = design, plot = TRUE)
   lcpm=v$E 

the extracted counts will be free of batch effect and unwanted variation?v

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No, it does not mean that. voom() uses the design matrix with the W covariates, to compute precision weights but not to adjust the log-cpm values. Adjustment for batch factors, such as the W matrix from RUV, is done during the differential expression analysis. At no stage are the original counts or log-cpm values adjusted.

I gave you the correct answer three weeks ago. If you want log-cpm values that are batch adjusted, you need to use cpm and removeBatchEffect.

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