bulk-rna-seq-differential-expression-with-omicverse（使用 omicverse 进行批量 rna seq 差异表达）

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.

1. 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.

1. 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.

1. Map gene identifiers

- Run ov.bulk.Matrix_ID_mapping(counts_df, 'genesets/pair_<GENOME>.tsv') to replace gene_id entries with gene symbols.

1. Initialise the DEG object

- Create dds = ov.bulk.pyDEG(mapped_counts). - Handle duplicate gene symbols with dds.drop_duplicates_index() to keep the highest expressed version.

1. Normalise and estimate size factors

- Execute dds.normalize() to calculate DESeq2 size factors, correcting for library size and batch differences.

1. Run differential testing

- Collect treatment and control replicate labels into lists. - Call dds.deg_analysis(treatment_groups, control_groups, method='ttest') for the default Welch t-test. - Offer optional alternatives: method='edgepy' for edgeR-like tests and method='limma' for limma-style modelling.

1. Filter and threshold results

- Note that lowly expressed genes are retained by default; filter using dds.result.loc[dds.result['log2(BaseMean)'] > 1] when needed. - Set dynamic fold-change and significance cutoffs via dds.foldchange_set(fc_threshold=-1, pval_threshold=0.05, logp_max=6) (fc_threshold=-1 auto-selects based on log2FC distribution).

1. Visualise differential expression

- Produce volcano plots with dds.plot_volcano(title=..., figsize=..., plot_genes=... or plot_genes_num=...) to highlight key genes. - Generate per-gene boxplots using dds.plot_boxplot(genes=[...], treatment_groups=..., control_groups=..., figsize=..., legend_bbox=...); adjust y-axis tick labels if required.

1. Perform pathway enrichment (optional)

- Download curated pathway libraries through ov.utils.download_pathway_database(). - Load genesets with ov.utils.geneset_prepare(<path>, organism='Mouse'|'Human'|...). - Build the DEG gene list from dds.result.loc[dds.result['sig']!= 'normal'].index. - Run enrichment with ov.bulk.geneset_enrichment(gene_list=deg_genes, pathways_dict=..., pvalue_type='auto', organism=...). Encourage users without internet access to provide a background gene list. - Visualise single-library results via ov.bulk.geneset_plot(...) and combine multiple ontologies using ov.bulk.geneset_plot_multi(enr_dict, colors_dict, num=...).

1. Document outputs

- Suggest exporting dds.result and enrichment tables to CSV for downstream reporting. - Encourage users to save figures generated by matplotlib (plt.savefig(...)) when running outside notebooks.

1. Defensive validation # Before DEG: verify treatment/control groups exist as column names all_cols = set(dds.result.columns) if hasattr(dds, 'result') else set(counts_df.columns) for g in treatment_groups + control_groups: assert g in all_cols, f"Sample '{g}' not found in count matrix columns" # Verify groups don't overlap assert not set(treatment_groups) & set(control_groups), "Treatment and control groups must not overlap"
2. Troubleshooting tips

- Ensure sample labels in treatment_groups/control_groups exactly match column names post-cleanup. - Verify required packages (omicverse, pyComplexHeatmap, gseapy) are installed for enrichment visualisations. - Remind users that internet access is required the first time they download gene mappings or pathway databases.

Examples

• "I have a featureCounts matrix for mouse tumour samples—normalize it with DESeq2, run t-test DEG, and highlight the top 8 genes in a volcano plot."
• "Use omicverse to compute edgeR-style differential expression between treated and control replicates, then run GO enrichment on significant genes."
• "Guide me through converting Ensembl IDs to symbols, performing limma DEG, and plotting boxplots for Krtap9-5 and Lef1."

References

• Detailed walkthrough notebook: t_deg.ipynb
• Sample count matrix for testing: sample/counts.txt
• Quick copy/paste commands: reference.md