archs4-database
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
npx skills add BioTender-max/awesome-bio-agent-skills --skill archs4-database --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Instructs the agent to interact with ARCHS4 REST API to retrieve gene-level expression z-scores across tissues, find co-expressed genes, search RNA-seq samples by metadata, and obtain gene metadata. It also includes handling for HDF5 bulk data download and plotting utilities.
How it works
- Provides functions to call the ARCHS4 API endpoints such as /meta/genes/{gene}/zscore for tissue z-scores, /meta/genes/{gene}/correlations for co-expression, /samples/search for sample metadata queries, and /meta/genes/{gene} for gene metadata.
- Demonstrates quick-start usage via Python code that fetches data, builds DataFrames, and sorts results by z-score or correlation.
- Includes example workflows: multi-gene tissue expression heatmaps, extracting a co-expression gene list, and plotting tissue expression barplots.
- Describes HDF5 bulk data access with download URLs and a sample routine to extract gene expression values from HDF5 files using h5py (with prerequisites and error handling).
When to use it
- When you need tissue-specific or cell-type-specific expression z-scores for a gene across many tissues.
- When you want genes co-expressed with a query gene for network or pathway inference.
- When you need to search RNA-seq samples by tissue, disease, or metadata keyword to identify datasets.
- When you require accessing precomputed HDF5 matrices for bulk analyses.
What it can touch
- REST API endpoints under ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" for zscore, correlations, samples/search, and gene metadata.
- HDF5 files downloadable from the ARCHS4 data portal (human_gene_v2.6.h5, mouse_gene_v2.6.h5, etc.).
- Local Python packages (requests, pandas, matplotlib, seaborn) and optional h5py for HDF5 handling.
Caveats
- Rate limits noted: ~10 requests/second; a brief sleep between calls is suggested to avoid throttling.
- HDF5 access requires downloading large files (30–60 GB disk space for bulk analysis).
- HDF5 example relies on h5py being installed; otherwise a fallback message is produced.
- Gene symbols are expected in HGNC format for human and appropriate symbols for mouse; mismatches return empty results.
# ARCHS4 Database ## Overview ARCHS4 (All RNA-seq and ChIP-seq Sample and Signature Search) is a resource of uniformly aligned and processed human and mouse RNA-seq data from NCBI GEO and SRA, covering 1 million+ samples. The REST API at `https://maayanlab.cloud/archs4/api/` provides gene-level expression profiles, z-score normalized tissue expression, co-expression networks, and sample metadata search — all without authentication. Large-scale bulk queries can also use the downloadable HDF5 expression matrices. ## When to Use - Retrieving tissue-specific or cell-type-specific expression z-scores for a gene of interest across hundreds of tissue types - Finding genes co-expressed with a query gene (co-expression network construction or guilt-by-association analysis) - Searching for RNA-seq samples by tissue, disease, or metadata keyword to identify candidate datasets for reanalysis - Comparing expression profiles of multiple genes across tissues to prioritize candidates for wet-lab follow-up - Accessing uniformly processed gene expression matrices (HDF5 format) for large-scale cross-study analysis - Validating differential expression results by checking whether a gene's expression di
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Query 1: Gene Expression Z-Scores Across Tissues
- Query 2: Co-expressed Genes
- Query 3: Sample Search
- Query 4: Gene-Level Metadata Summary
- Query 5: Visualization — Tissue Expression Barplot
- Query 6: HDF5 Bulk Data Access
- Key Concepts
- Z-Score Normalization
- HDF5 vs REST API
pip install requests pandas matplotlib seaborn
What does the archs4-database skill do?
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill archs4-database --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.