bio-de-deseq2-basics

To run DESeq2 for differential expression analysis, you provide an RNA-seq count matrix and matching sample metadata to construct a DESeqDataSet, execute the standard DESeq workflow, and extract results with adjusted p-values and log2 fold changes.

Yes, you can account for batch effects or multiple conditions in your DESeq2 design formula by specifying the variables from your sample metadata when constructing the DESeqDataSet object for differential expression analysis.

You apply lfcShrink after running the standard DESeq workflow to generate stable log2 fold changes for your RNA-seq differential expression results, ensuring more accurate ranking and visualization of differentially expressed genes.

The best way to prepare RNA-seq count matrices for DESeq2 is ensuring they contain raw integer counts with matching sample metadata columns, allowing the workflow to properly construct the DESeqDataSet and perform pre-filtering before differential expression analysis.

Yes, DESeq2 supports exporting sorted differential expression results by extracting the output after applying lfcShrink, allowing you to sort by log2 fold changes or adjusted p-values and export the final list of differentially expressed genes.

You need matching sample metadata to identify differentially expressed genes because DESeq2 requires this information to construct the DESeqDataSet and define the design formula that models biological conditions and batch effects during differential expression analysis.