Hi everyone. We were hoping to find some help using edgeR or DEXSeq to detect/quantify splice variants from a multi factor RNA-Seq experiment.
We have a multifactor RNA-seq experiment that involves two disease states and four time points for each patient (e.g. 4 samples per SAME patient -- basically, a "paired" design with 4 samples instead of 2). We have already successfully completed differential gene expression analysis using the limma/voom pipeline (and edgeR), in which we were able to use the block argument to block the patients and account for this factor in the linear model. We are now interested in looking at differential splicing using either edgeR (spliceVariants function) or DEXSeq. We have summarized reads by exon counts and are ready to proceed with differential analysis, but it's not entirely clear how to pass multiple factors (e.g. disease state, time point, patient) into these workflows.
Can this be done with either tool (edgeR or DEXSeq)? If not, any suggestions for how best to analyze splicing changes in these samples?
Thanks for the help.
We have a multifactor RNA-seq experiment that involves two disease states and four time points for each patient (e.g. 4 samples per SAME patient -- basically, a "paired" design with 4 samples instead of 2). We have already successfully completed differential gene expression analysis using the limma/voom pipeline (and edgeR), in which we were able to use the block argument to block the patients and account for this factor in the linear model. We are now interested in looking at differential splicing using either edgeR (spliceVariants function) or DEXSeq. We have summarized reads by exon counts and are ready to proceed with differential analysis, but it's not entirely clear how to pass multiple factors (e.g. disease state, time point, patient) into these workflows.
Can this be done with either tool (edgeR or DEXSeq)? If not, any suggestions for how best to analyze splicing changes in these samples?
Thanks for the help.
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