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Limma differential expression analysis with median-centered data

Hi all,

I've just received a matrix of RNA_seq data (genes at the rows, samples at the columns and values at each cell), where the values at the cells are median-centered values. I would like to know if it's possible to perform a differential expression analysis using limma with this data.

Thanks in advance,

Joan

r next-gen rna-seq
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updated by Ram Ram • written by jfrigola1 • jfrigola1

It'd be simpler if whomever gave you the dataset could simply provide you with either the raw or estimated counts. Then you don't have to monkey around with things as much.

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updated by Ram Ram • written by Devon Ryan Devon Ryan

1 answer

There is a way to do with limma as its written in the limma manual but for that you would need to have replicates and you can group them to perform the analysis.

I would suggest to compute pair wise comparison of median-centered values of genes as their row wise Z-score (expression value subtracted by mean of its row values and divided by its standard deviation). This is easy also on R.

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updated by Ram Ram • written by Manvendra Singh Manvendra Singh

Sorry for the very late comment, after almost 3 years. Do you mean something like this?

exprs.gse <- t(scale(t(exprs(gse))))

After that you can input exprs.gse in lmFit, followed by eBayes?

PS: The data I am working with is from microarray and have been "log2 transformed (signal-to-noise < 3 or flag values >8191 set to missing), centered and scaled, missing values imputed with k-nearest neighbors imputation, batch-adjusted using an ANOVA-based algorithm (pamr) and mean-centered within-array (column wise)."

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updated by Ram Ram • written by pedromilanez • pedromilanez

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