15 days ago by
Advice about combining experiments: https://support.bioconductor.org/p/91560/. There is much more about this topic at the Bioconductor forum in the context of what experiments represent: how to model the experiment based on content/samples, how to generate counts, when and at which step to normalize (or not), plus QA options.
Example Galaxy workflows that can be modified to suit your use-case: https://galaxyproject.github.io/training-material/topics/transcriptomics/. This one also exists but is not quite ready for publication, however, it can be used as a reference until then: https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/limma-voom_fastqs_to_counts/tutorial.html
Some of the tools available in Galaxy for DE analysis:
Get the counts:
- featureCounts Measure gene expression in RNA-Seq experiments from SAM or BAM files.
Then use one or more of these. Help is on the tool forms, with more details in the Bioconductor FAQs/Forum:
- limma Perform differential expression with limma-voom or limma-trend
- edgeR Perform differential expression of count data
- DESeq2 Determines differentially expressed features from count tables
Thanks! Jen, Galaxy team