Does the Section 2.3 Differential expression analysis in RUVSeq vignette work in case of a global shift in exprssion?
And can normalized counts obtained by normCounts(set1) be comparable directly between samples? Or should be transformed to CPM, TPM...?
in the case of a global shift of expression the preliminary scaling step that we show in Section 2.3 should be avoided, as it assumes that the majority of the genes are not DE.
In practice, it means to avoid the betweenLaneNormalization and the calcNormFactor steps. You may want to set the all the size factors to 1 in edgeR so that no offset is added (the factors inferred by RUV should be enough).
As for your second question: yes, the normCounts() function returns normalized expression levels that are comparable across samples in the same dataset (but not across genes).
Thanks, Davide! The last thing I wanna confirm is "across samples" means comparing the same gene in different samples, and "across genes" means comparing different genes in one sample, right?
Thanks, Davide! The last thing I wanna confirm is "across samples" means comparing the same gene in different samples, and "across genes" means comparing different genes in one sample, right?
That's correct!