القائمة
Ancestry decomposition PCA against the Simons Genome Diversity Project
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استنادا إلى تصنيف SOC المهني
| name | claw-ancestry-pca |
| version | 0.1.0 |
| description | Ancestry decomposition PCA against the Simons Genome Diversity Project |
| author | Manuel Corpas |
| license | MIT |
| tags | ["population-genetics","PCA","ancestry","SGDP","global-diversity"] |
| inputs | [{"name":"vcf","type":"file","format":["vcf","vcf.gz"],"description":"VCF file with genotype data for your study cohort"},{"name":"pop-map","type":"file","format":["tsv","txt"],"description":"Tab-separated file mapping sample IDs to population labels"}] |
| outputs | [{"name":"figure","type":"file","format":["png","pdf"],"description":"Multi-panel PCA composite figure showing ancestry decomposition"},{"name":"report","type":"file","format":"markdown","description":"Ancestry analysis report with population assignments and statistics"}] |
| metadata | {"genetind":{"category":"bioinformatics","homepage":"https://github.com/ClawBio/ClawBio","min_python":"3.9","dependencies":["pandas","numpy","matplotlib","scikit-learn","adjustText"],"system_dependencies":["plink","bcftools"]}} |
Place your study cohort in global genetic context by computing a joint PCA against the Simons Genome Diversity Project (SGDP) — 345 samples from 164 populations spanning every inhabited continent.
If you ask ChatGPT to "run a PCA against a global reference panel," it will:
This skill encodes the correct methodological decisions:
The skill bundles the SGDP v4 dataset (Mallick et al., 2016, Nature):
python ancestry_pca.py \
--vcf your_cohort.vcf.gz \
--pop-map your_populations.tsv \
--output ancestry_report
python ancestry_pca.py --demo --output demo_report
The demo uses pre-computed PCA results from the Peruvian Genome Project (736 samples, 28 populations) and generates the full 4-panel figure instantly.
Ancestry Decomposition PCA
==========================
Cohort: 736 samples, 28 populations
Reference: SGDP (345 samples, 164 populations)
Common variants: 42,831 biallelic SNPs
Variance explained:
PC1: 51.44% PC2: 21.70% PC3: 6.70%
Panel D — Global Context:
Cohort samples cluster between European and East Asian
reference populations, with Amazonian groups showing
distinct positioning from Highland and Coastal groups.
Figures saved to: ancestry_report/
Figure3_PCA_composite.png (300 dpi)
Figure3_PCA_composite.pdf (vector)
Reproducibility:
commands.sh | environment.yml | checksums.sha256
If you use this skill in a publication, please cite:
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