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tcga-bulk-data-preprocessing-with-omicverse

Load TCGA GDC downloads into an AnnData object with normalized expression and clinical metadata.

32|5|Updated Mar 12, 2026
Search Tags:#data-processing#survival-analysis#rna-seq#tcga#ann-data#clinical-metadata
One-click install
npx skills add https://github.com/omicverse/omicclaw --skill tcga-bulk-data-preprocessing-with-omicverse-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tcga-bulk-data-preprocessing-with-omicverse
Source: https://github.com/omicverse/omicclaw/tree/main/src/omicverse_skills/skills/tcga-preprocessing
Command: npx skills add https://github.com/omicverse/omicclaw --skill tcga-bulk-data-preprocessing-with-omicverse-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill helps researchers load TCGA data from GDC downloads, build normalised expression matrices (counts, FPKM, TPM), attach clinical metadata, and enable downstream survival analyses through ov.bulk.pyTCGA.

Core Features & Use Cases

  • Load TCGA data from GDC downloads and decompress archives.
  • Build a unified AnnData object with counts, FPKM, and TPM layers and attach patient metadata.
  • Run Kaplan-Meier survival analyses and export enriched results for further exploration.

Quick Start

Load your TCGA GDC downloads, initialize the AnnData object with ov.bulk.pyTCGA, run survival analysis, and export the enriched dataset.

Frequently Asked Questions about tcga-bulk-data-preprocessing-with-omicverse

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I preprocess TCGA bulk RNA-seq data from GDC downloads into an AnnData object?▼

You can preprocess TCGA bulk RNA-seq data by loading GDC downloads, decompressing archives, and using the ov.bulk.pyTCGA API to build a normalized expression matrix with counts, FPKM, and TPM layers in an AnnData object.

What is the best way to attach clinical metadata to a TCGA expression matrix for survival analysis?▼

The best way to attach clinical metadata is initializing the ov.bulk.pyTCGA API with your TCGA GDC downloads, which automatically builds a unified AnnData object linking patient metadata to expression layers for survival workflows.

Can I run Kaplan-Meier survival analysis directly on TCGA data loaded from GDC?▼

Yes, you can run Kaplan-Meier survival analyses directly on TCGA data by loading GDC downloads into the ov.bulk.pyTCGA API, which attaches the necessary patient clinical metadata to the normalized expression matrix.

Does the OmicVerse pyTCGA workflow support multiple normalization layers like FPKM and TPM?▼

Yes, the OmicVerse pyTCGA workflow supports multiple normalization layers by building a unified AnnData object that simultaneously contains counts, FPKM, and TPM layers for TCGA bulk RNA-seq projects across cancer types.

Do I need to manually decompress TCGA archives before building a normalized expression matrix?▼

No, you do not need to manually decompress TCGA archives, because the preprocessing pipeline loads TCGA data directly from GDC downloads and automatically decompresses archives to build the normalized expression matrix.

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