deseq2-differential-expression

Use DESeq2 to identify differentially expressed genes in RNA-seq count data by applying negative binomial generalized linear models with empirical Bayes shrinkage for robust statistical inference between experimental conditions.

Yes, DESeq2 supports complex experimental designs including multi-factor experiments, batch effects, and interaction terms to accurately isolate condition-specific gene expression changes from confounding variables.

You can generate MA plots, volcano plots, and heatmaps to visually interpret RNA-seq differential expression results and identify up-regulated or down-regulated genes across experimental conditions.

DESeq2 models bulk RNA-seq count data using negative binomial generalized linear models and applies empirical Bayes shrinkage to provide robust statistical inference for identifying differentially expressed genes.

Use DESeq2 to perform differential expression analysis on bulk RNA-seq count matrices from treated and control groups to statistically identify which genes are activated or suppressed by the drug treatment.

Yes, you need a bulk RNA-seq count matrix and associated metadata to run DESeq2, which utilizes tximport and tximeta to process transcript-level counts into the matrix required for statistical testing.