Bulk RNA-seq differential expression with omicverse

Overview

Follow this skill to run the end-to-end differential expression (DEG) workflow showcased in t_deg.ipynb. It assumes the user provides a raw gene-level count matrix (e.g., from featureCounts) and wants to analyse bulk RNA-seq cohorts inside omicverse.

Instructions

1. Set up the session
   • Import omicverse as ov, scanpy as sc, and matplotlib.pyplot as plt.
   • Call ov.plot_set() so downstream plots adopt omicverse styling.
2. Prepare ID mapping assets
   • When gene IDs must be converted to gene symbols, instruct the user to download mapping pairs via ov.utils.download_geneid_annotation_pair() and store them under genesets/.
   • Mention the available prebuilt genomes (T2T-CHM13, GRCh38, GRCh37, GRCm39, danRer7, danRer11) and that users can generate their own mapping from GTF files if needed.
3. Load the raw counts
   • Read tab-delimited featureCounts output with ov.pd.read_csv(..., sep='\t', header=1, index_col=0).
   • Strip trailing .bam segments from column names using list comprehension so sample IDs are clean.
4. Map gene identifiers
   • Run ov.bulk.Matrix_ID_mapping(counts_df, 'genesets/pair_<GENOME>.tsv') to replace gene_id entries with gene symbols.
5. Initialise the DEG object
   • Create dds = ov.bulk.pyDEG(mapped_counts).