differential gene expression analysis
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2.9 years ago
ATCG ▴ 300

I'm trying to do differential gene expression analysis (RNA-Seq data) using Limma and EdgeR How can I adjust my count matrix for technical confounders(RIN, batch)?

a. Is it possible to adjust for confounders within Limma?

b. Can I adjust the count matrix for covariates using linear regression and import the residuals for downstream differential expression analysis using Limma or EdgeR? Thanks!

confounders RNA-Seq Limma EdgeR Regression • 1.4k views
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"Yes, typically one simply adds batch and RIN as covariates if they seem relevant. " Do you mean that you can add covariates into the model matrix e.g. design<-model.matrix(~group+batch+RIN) Thanks!

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Yes, correct

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Is there a way to visualize the data (residuals) using PCA or MDS plot after performing the linear modeling? I want to do this to confirm the samples no longer cluster by batch.

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2.9 years ago
  1. Yes, typically one simply adds batch and RIN as covariates if they seem relevant. You could also use combat from the SVA package if really necessary. In limma there's also a removeBatchEffects() function, though that wouldn't handle RIN.
  2. See the SVA package in R.
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Actually removeBatchEffects() can handle numeric covariates like RIN easily enough. Just set batch=batch and covariates=RIN in the removeBatchEffects call. The covariates argument wasn't an original part of the removeBatchEffects function, but was introduced with the April 2012 Bioconductor release.

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2.9 years ago
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Devon, How can I plot the data going into the analysis? design<-model.matrix(~group+batch+RIN) will include RIN in the model and this should change the way samples cluster by PCA or MDS plot. Is there a way to extract the corrected counts matrix? Thank you!

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Including things in a model will have absolutely no effect on PCA or MDS. Check some residuals or some scatter plots if you want to QC how well the correction is being done.

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There's no such thing as a corrected counts matrix, but one can correct the logCPM values. To see visually how the samples cluster after adjustment for batch and RIN:

logCPM <- cpm(y,log=TRUE)
logCPM.corrected <- removeBatchEffect(logCPM, batch=batch, covariates=RIN,
                                  design=model.matrix(~group))
plotMDS(logCPM.corrected)

I would do this the same way regardless of whether you are using limma or edgeR for the DE analysis. Note that logCPM.corrected is only for plotting purposes --- don't use it for the DE analysis.

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