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tcga-expression-for-gene

Query live GDC data to plot single-gene expression across TCGA cancer cohorts.

64|12|Updated Apr 7, 2026
Search Tags:#visualization#gene-expression#tcga#gdc#pan-cancer#tumor-vs-normal
One-click install
npx skills add https://github.com/MDhewei/bioinfor-claw --skill tcga-expression-for-gene
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tcga-expression-for-gene
Source: https://github.com/MDhewei/bioinfor-claw/tree/main/gene-centered-analysis/tcga_expression_for_gene
Command: npx skills add https://github.com/MDhewei/bioinfor-claw --skill tcga-expression-for-gene

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, requests, and includes scripts (resource) components.

What problem does it solve?

Query TCGA expression data for a single gene across all cancer cohorts and generate comparative visuals.

Core Features & Use Cases

  • Pan-cancer overview: Generate a TCGA-wide expression landscape to identify cancers with the highest or lowest gene expression.
  • Cohort specifics: Download-and-plot expression distribution for a chosen TCGA cohort.
  • Tumor vs Normal: Compare tumor and normal tissue expression within a selected cohort.
  • Use in discovery pipelines to prioritize genes for survival or mutation analyses across cancer types.

Quick Start

Run a quick pan-cancer expression analysis for TP53 using the default settings and outdir to store results.

Frequently Asked Questions about tcga-expression-for-gene

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

FAQPage Schema
How do I visualize TCGA gene expression across multiple cancer cohorts?▼

To visualize TCGA gene expression across multiple cancer cohorts, query live GDC data with a gene symbol and select pan_cancer mode to generate bar plots showing expression levels across cancer types.

Can I compare tumor vs normal tissue expression for a specific TCGA cohort?▼

Yes, you can compare tumor vs normal tissue expression by selecting the tumor_vs_normal mode, providing a gene symbol and cancer type, which produces comparative plots from live GDC data.

What's the best way to download and plot expression distribution for a single TCGA cohort?▼

The best way to plot expression distribution for a single TCGA cohort is using the single_cohort mode, which fetches live GDC data for your specified gene symbol and cancer type to generate distribution plots.

Do I need Python dependencies like pandas and matplotlib to run TCGA expression analysis?▼

Yes, you need pandas, matplotlib, and requests installed to fetch live GDC data, process expression dataframes, and generate visualizations for pan-cancer, single cohort, or tumor vs normal comparisons.

What inputs are required to generate a pan-cancer TCGA expression landscape?▼

Generating a pan-cancer TCGA expression landscape requires a gene symbol, a mode choice like pan_cancer, and an output directory to store the resulting bar plot visualizations.

Can I use TCGA expression analysis to identify genes for survival or mutation analyses?▼

Yes, you can use the TCGA expression analysis in discovery pipelines to identify cancers with highest or lowest gene expression, helping prioritize genes for downstream survival or mutation analyses.

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