PyDESeq2, a Python implementation of the DESeq2 method originally developed in R, is a versatile tool for conducting differential expression analysis (DEA) on bulk RNA-seq data. This reimplementation provides similar—but not identical—results: it achieves higher model likelihood and enables faster performance on large datasets.

Normalization:
• Before differential expression analysis: Median-of-ratios normalization (dds.deseq2()) is used to correct for differences in sequencing depth across samples. • After differential expression analysis: VST (Variance Stabilizing Transformation) is applied for exploratory analyses such as PCA, clustering, and heatmaps.

The Wald test is used to compare two conditions:
The null hypothesis of the Wald test states that for each gene, there is no differential expression between two sample groups (e.g., treated vs. control). If the p-value is small (e.g., p < 0.05), the null hypothesis is rejected, suggesting there is only a 5% chance that the observed difference occurred by random chance. However, when testing many genes, a number of non-differentially expressed genes may still appear significant due to random chance (false positives).

Visual outputs:
• pydesq2_results_table.csv — DE results sorted by log2FoldChange (raw Wald values).
• Volcano plot Volcano_<contrast> (red = sig, grey = non-sig).
• Heat-map Heatmap_<contrast> for the top-50 DE genes (VST counts).
• Global PCA: `.

Example of metadata file:
Sample forward-absolute-filepath reverse-absolute-filepath Condition Replicate Batch SRR13978645 SRR13978645_1.fastq.gz SRR13978645_2.fastq.gz CTRL R1 Batch1 SRR13978644 SRR13978644_1.fastq.gz SRR13978644_2.fastq.gz CTRL R2 Batch1 SRR13978643 SRR13978643_1.fastq.gz SRR13978643_2.fastq.gz CTRL R3 Batch2 SRR13978642 SRR13978642_1.fastq.gz SRR13978642_2.fastq.gz SPRC1 R1 Batch1 SRR13978641 SRR13978641_1.fastq.gz SRR13978641_2.fastq.gz SPRC2 R1 Batch1 SRR13978640 SRR13978640_1.fastq.gz SRR13978640_2.fastq.gz SPRC2 R2 Batch2