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Batch effect in RNAseq

PCA plot of triplicates

Figure A

enter image description here Figure B

The distribution of replicates from an RNA-seq study is plotted on the PCA plot as shown in the figures (plotted using top 500 highly variant genes).
(1) Figure A: Looking at the distribution what is your opinion on carrying out a DE analysis. What type of preprocessing of these samples (please suggest packages/tools) will help in carrying out DE? (2) Figure B: There is a strong batch effect as the variance between batch is higher than the variance between the conditions compared. I was thinking of comparing sample and treatment of the same batch independently using Gfold or NOIseq and then pull out genes which show similar fold changes between the 3 comparisons. Would like to have an opinion on that. Please comment.

rna-seq
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updated by Devon Ryan Devon Ryan • written by sure • sure

1 answer

The standard methods for dealing with a batch effect are:

  1. Add it to you model (e.g., in DESeq2, limma, or edgeR)
  2. Use something like Combat from the SVA package.

I personally prefer option 1 except in cases where the batch effect is expected to be fairly variable by sample.

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written by Devon Ryan Devon Ryan

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