From 4294381c64f2a01c552d58bdf36dd7ef95fc8184 Mon Sep 17 00:00:00 2001 From: Geo Pertea Date: Sat, 21 Sep 2024 21:09:09 -0400 Subject: [PATCH] cleanup some comments from vignette --- vignettes/Intro_qsvaR.Rmd | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/vignettes/Intro_qsvaR.Rmd b/vignettes/Intro_qsvaR.Rmd index 3ecafa2..e5da174 100644 --- a/vignettes/Intro_qsvaR.Rmd +++ b/vignettes/Intro_qsvaR.Rmd @@ -222,11 +222,8 @@ dim(qsvs_wrapper) Next we can use a standard limma package approach to do differential expression on the data. The key here is that we add our qsvs to the `model.matrix()`. Here we input our `RangedSummarizedExperiment` object and our `model.matrix()` with qSVs. Note here that the `RangedSummarizedExperiment` object is the original object loaded with the full list of transcripts, not the the one we subsetted for qSVs. This is because while PCs can be generated from a subset of genes, differential expression is best done on the full dataset. The expected output is a `sigTx` object that shows the results of differential expression. ```{r "perform DE"} -### should be done by cbinding mod to pcs -## subset to an expression cutoff -#rse_tx <- rse_tx[rowData(rse_tx)$passExprsCut, ] -# create a model.matrix with demographic infor and qsvs +# create a model.matrix with demographic info and qsvs mod_qSVA <- cbind(mod, qsvs) # log tranform transcript expression