Individual skill
pca-dimensionality-reduction
Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.
awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
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- cancer-genomics Repository topic
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- differential-expression Repository topic
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SKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 bytesSKILL.mdcore · 6,029 byteseval_report_pca-dimensionality-reduction_result.jsonasset · 15,039 bytesreferences/algorithm.mddoc · 4,061 bytesreferences/cli-guide.mddoc · 3,845 bytesreferences/troubleshooting.mddoc · 3,912 bytesscripts/functions.Rasset · 7,975 bytesscripts/main.Rruns · 3,724 bytesscripts/run_analysis.Rasset · 5,343 bytesscripts/utils.Rasset · 5,438 bytestests/data/sample_pca_1.csvasset · 429 bytestests/data/sample_pca_2.csvasset · 3,344,669 bytestests/data/sample_pca_3.csvasset · 228 bytes
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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
# PCA Dimensionality Reduction Analysis
Use this skill to run principal component analysis on a tabular dataset and export explained variance, sample scores, feature loadings, and diagnostic figures.
## Use This Skill When
- You need to reduce multiple numeric variables into a smaller set of principal components.
- You need a command-line PCA workflow with parameter validation.
- You need standardized output files for downstream analysis.
## Primary Command
```bash