rnaseq-differential-expression

To detect differentially expressed genes from RNA-seq count data, provide a gene-by-sample count matrix and metadata table specifying two groups. The analysis applies DESeq2-style methods via pydeseq2 on raw counts, returning full results and publication-ready plots.

You can run differential expression analysis on normalized expression data by using Welch's t-test or Mann-Whitney U tests on log2(CPM+1) values. This provides a faster non-parametric alternative when raw count matrices are unavailable.

RNA-seq differential expression results generate volcano plots, MA plots, and a top-genes heatmap. These publication-ready visualizations accompany full tabular outputs including de_results.tsv and significant_genes.tsv for downstream analysis.

To prepare metadata for comparing two biological groups in DE analysis, create a table with sample and group columns. Define group-a and group-b labels to specify the comparison, then run the script to perform low-expression filtering and generate reports.

Pydeseq2 supports low-expression filtering as part of the RNA-seq differential expression workflow. The script performs this filtering on the gene count matrix before applying DESeq2-style analysis to identify significantly expressed genes between groups.

Use Welch's t-test instead of DESeq2 for differential expression analysis when working with pre-normalized log2(CPM+1) data rather than raw counts. It offers a faster non-parametric approach via the Mann-Whitney U test for identifying significant gene lists.