bio-geo-data
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
npx skills add BioTender-max/awesome-bio-agent-skills --skill geo-data --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.
## Version Compatibility Reference examples tested with: BioPython 1.83+, GEOparse 2.0+, R Bioconductor GEOquery 2.70+, pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show biopython geoparse` then introspect signatures - R: `packageVersion('GEOquery')` If the GSE structure doesn't match expectations (missing fields, malformed series matrix), re-fetch from FTP directly and inspect the SOFT or MINiML file as source of truth. # GEO Data **"Pull expression data from GEO accession GSE..."** -> GEO stores Series (GSE), Samples (GSM), Platforms (GPL), and curated DataSets (GDS, frozen 2018). The single most consequential decision is **processed (series matrix) vs raw (supplementary files / linked SRA)** — the answer turns on how much trust the submitter's normalization deserves. The single most-missed gotcha: **SuperSeries**. A GSE may be a meta-container (`!Series_relation = SuperSeries of: GSExxxxx`) holding multiple sub-studies on different platforms. Naively pulling samples from a SuperSeries gives mixed Affymetrix + Illumina + RNA-seq, mis-batched. - Python: `Entrez.esearch(db='gds')`, GEOparse for full series download - R:
- Version Compatibility
- Required Setup
- GEO record taxonomy
- The SuperSeries trap
- Decision matrix: processed vs raw vs SRA
- Series matrix files
- SOFT vs MINiML
- GEOparse vs GEOquery
- GEOmetadb status
- ArrayExpress -> BioStudies migration (2020)
- Code patterns
- Search GEO for studies matching a query
- Detect SuperSeries before pulling data
- Download series matrix with submitter caveat
pip install biopython GEOparse pandas OR for R-side:
What does the bio-geo-data skill do?
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill geo-data --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.