# Normal Tissue Expression For Gene > Retrieve and summarize normal tissue expression for a single human gene using GTEx, including full tissue-level expression, publication-quality barplot output, and a simple expression-pattern classification such as universally expressed, tissue-specific, mixed, or non-expressed. Use when this capability is needed. - Skill: `tomevault-io/normal-tissue-expression-for-gene` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/normal-tissue-expression-for-gene` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/normal-tissue-expression-for-gene/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/normal-tissue-expression-for-gene --- # Normal Tissue Expression for Gene ## Purpose Use this skill to retrieve and summarize **normal tissue expression for a single gene**. This skill is intended for common daily bioinformatics questions such as: - In which normal tissues is this gene highly expressed? - Is this gene broadly expressed across tissues or tissue-specific? - Is this gene lowly expressed or effectively non-expressed in GTEx? - What does the expression distribution across all normal tissues look like? This skill is **single-gene focused** by design. It is optimized for fast, interpretable inspection of one gene at a time rather than large matrix-style gene-list analysis. ## Scope This skill covers: - **normal tissue expression** - **GTEx-based tissue-level summaries** - **publication-quality visualization** - **basic expression-pattern classification** This skill does **not** cover: - cancer cell line expression - tumor expression - protein abundance - gene essentiality - pathway enrichment - multi-gene heatmaps as the primary use case Those should be handled by separate skills. ## Data source Primary data source: - **GTEx normal tissue expression** The implementation should prefer **official GTEx API access** when available. Recommended retrieval flow: 1. Resolve the queried gene against GTEx gene reference data 2. Retrieve median tissue expression values for that gene 3. Normalize the returned fields into a consistent table 4. Generate summary outputs and figures ## When to use Use this skill when the user asks things like: - “Show me normal tissue expression of TP53.” - “Which tissues highly express EGFR?” - “Is GATA3 tissue-specific?” - “How broadly is MYC expressed across tissues?” - “Plot GTEx tissue expression for this gene.” ## When not to use Do not use this skill when the user asks for: - cancer cell line expression - tumor expression - gene-list heatmaps as the main deliverable - protein expression - CRISPR dependency - gene annotation unrelated to tissue expression Use another skill for those requests. ## Input ## Required input ### `--gene` A single gene symbol. Examples: - `TP53` - `EGFR` - `ERBB2` - `GATA3` ## Optional input ### `--top-n` Number of top tissues to summarize in the text summary. Default: - `10` ### `--expr-detect-threshold` Expression threshold used to define whether a tissue is considered “detected” or expressed. Default: - `1.0` This threshold is also shown on the barplot. ### `--outdir` Output directory for all generated files. --- ## Why this skill is gene-level instead of gene-list-level This skill is intentionally centered on **one gene at a time**, because the most common practical use case is: 1. the user has a gene of interest, 2. they want to quickly inspect its tissue distribution, 3. they want an interpretable classification and figure. For this use case, a single-gene workflow is usually more useful than a matrix-first workflow. A multi-gene normal-tissue-expression skill can be built separately if needed. ## Outputs A successful run should produce: - one tissue-level expression table - one concise summary text file - one summary TSV file - one publication-quality barplot in PNG - one publication-quality barplot in PDF ### Typical output files - `.gtex_tissues.tsv` - `summary.txt` - `gene_expression_summary.tsv` - `.gtex_all_tissues.png` - `.gtex_all_tissues.pdf` ## Output table structure ### Tissue-level table The per-gene tissue table should contain: - `gene_symbol` - `gencode_id` - `tissue` - `expression` - `rank_within_gene` ### Summary table The summary TSV should contain: - `query_gene` - `resolved_gene_symbol` - `gencode_id` - `expression_category` - `max_expression` - `median_expression_across_tissues` - `num_tissues` - `num_detected_tissues` - `fraction_detected_tissues` ## Core workflow ### Step 1. Parse and normalize the input gene - Read the gene from `--gene` - Normalize formatting - Treat the query as a human gene symbol ### Step 2. Resolve the gene in GTEx - Query GTEx reference gene information - Resolve: - canonical gene symbol - GENCODE ID If the gene cannot be resolved, fail clearly. ### Step 3. Retrieve GTEx normal tissue expression - Query GTEx median tissue expression - Normalize the response into a consistent table with: - tissue - expression ### Step 4. Rank and summarize tissues - Sort tissues by expression - Keep all tissues in the output table - Use `top-n` only for the short text summary ### Step 5. Classify the expression pattern Assign one of the following categories: - `universally-expressed` - `tissue-specific` - `mixed` - `non-expressed` - `no-data` ### Step 6. Generate a publication-quality barplot - Show **all tissues by default** - Use a horizontal barplot - Draw a threshold line for the expression detection threshold - Save as both PNG and PDF ### Step 7. Write output files Write the tissue table, summary files, and figure to `outdir`. ## Expression classification logic This skill should provide a **simple, interpretable classification** rather than an overly complicated tissue-specificity score. Recommended logic: ### `non-expressed` Use when: - the maximum tissue expression is below the detection threshold ### `tissue-specific` Use when: - maximum expression is clearly high - expression in the top tissue is much higher than the median across tissues - only a relatively small fraction of tissues are above the detection threshold ### `universally-expressed` Use when: - most tissues are above the detection threshold - median expression across tissues is not low ### `mixed` Use when: - the gene is expressed in some tissues - but does not strongly fit either universal or highly tissue-specific behavior ### `no-data` Use when: - no GTEx result is returned for the gene ## Threshold guidance The default expression detection threshold is: - **1.0** This is a practical and commonly used threshold for judging whether expression is present at a meaningful level. Interpretation: - below threshold: low or not detected - above threshold: detectable expression The threshold should also be displayed visually on the barplot. ## Plotting requirements The barplot should be **publication quality**. ### Plot style requirements - horizontal barplot - all tissues shown by default - tissues sorted by expression - high resolution - readable tissue labels - clean axes - minimal unnecessary styling - threshold line clearly visible ### Because the plot is horizontal Expression is on the x-axis, so the threshold should be plotted as a **vertical line**, not a horizontal line. ### Recommended outputs - PNG for quick viewing - PDF for publication or slides ## Execution ## Entry point ```bash python scripts/normal_tissue_expression_for_gene.py --gene "" --outdir --- > Source: [MDhewei/bioinfor-claw](https://github.com/MDhewei/bioinfor-claw) — distributed by [TomeVault](https://tomevault.io).