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38 minutes ago by

If you want to know about batch effects, which are often methodological, or in general any covariate, I think you should investigate on all of the genes because those type of biases will be present in all of your genes (i.e. your genes of interest a priori should not be biased towards being more or less affected by batches, etc.), and thus you will have more information (in fact, there are algorithms which precisely rely on looking at the "rest of genes" outside the comparison to model variance of unknown covariates)

If you have many variables and it becomes hard to look at all the PCA plots, you can screen them by performing correlations between the variables and the principal components. Plotting these correlations (in heatmap for example) will give you a quick look on how your experimental variables are associated to the PCs. As an example of what I mean, see the pcrplot in the ENmix package (section 11 of the user's guide)

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Papyrus640



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