[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ6D9A3N6849BT9VYFSBK5":3,"skill-same:01M6CZ6D9A3N6849BT9VYFSBK5":66},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":6,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":37,"install":38,"resources":51},"01M6CZ6D9A3N6849BT9VYFSBK5","stat-pairwise-correlation",null,"Compute pairwise correlations and significance tests across all numeric variables, and produce a correlation heatmap.","Scientific-Tooling\u002Fstructured-intelligence","Scientific-Tooling","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F267453265?v=4","01M6CZ1SE6GGD4F67EAQ0PVX38","Scientific Tooling","01M6CZ1SJKCFVGH10Z3A152K02",4,"MIT","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fstat-pairwise-correlation\u002FSKILL.md",[],[19,20,21,22,23,24,25],"correlation","stat pairwise correlation","produce","heatmap","compute","across","tests","2026-11-10 10:12:30",false,"---\nname: stat-pairwise-correlation\ndescription: Compute pairwise correlations and significance tests across all numeric variables, and produce a correlation heatmap.\n---\n\n# Skill: Pairwise Correlation Analysis\n\n## Use When\n\n- User wants to scan for linear or monotonic relationships among multiple numeric variables\n- User needs a correlation matrix with p-values for gene expression, neural activity, or metabolomics data\n- User wants to identify highly correlated features before modeling (collinearity check)\n- User is building a co-expression or co-activity network\n\n## Inputs\n\n- Required:\n  - Data matrix (CSV or TSV): rows = observations, columns = numeric variables\n- Optional:\n  - Correlation method: `pearson` (default), `spearman`, or `kendall`\n  - Multiple-testing correction method: `fdr` (Benjamini-Hochberg, default) or `bonferroni`\n  - Significance threshold for highlighting (default: `0.05`)\n  - Output directory (default: `.\u002Fcorr_output`)\n\n## Workflow\n\n1. Read the data matrix; keep only numeric columns.\n2. Compute the full pairwise correlation matrix using the specified method.\n3. Compute p-values for each pair using the appropriate test (Pearson: t-distribution; Spearman\u002FKendall: permutation or asymptotic).\n4. Apply multiple-testing correction across all variable pairs.\n5. Generate a correlation heatmap: color-coded by correlation coefficient, asterisks for significant pairs (adjusted p \u003C threshold).\n6. Generate a scatter plot matrix (pairs plot) for datasets with ≤ 10 variables.\n7. Report the top 20 strongest positive and top 20 strongest negative correlations.\n8. Write correlation matrix (TSV), adjusted p-value matrix (TSV), and plots (PDF) to output directory.\n9. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n\n## Output Contract\n\n- Correlation matrix (TSV): variable × variable, values = correlation coefficients\n- Adjusted p-value matrix (TSV): variable × variable, values = corrected p-values\n- Correlation heatmap (PDF)\n- Pairs plot (PDF, only if ≤ 10 variables)\n- Top correlations summary table (TSV): var1, var2, correlation, p_value, adj_p_value\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Pearson correlation assumes linearity; use Spearman or Kendall for non-linear monotonic relationships or ordinal data.\n- Correlation does not imply causation.\n- Kendall's tau is slow for n > 10000; Spearman is preferred for large samples.\n- Common failure: constant or near-constant columns produce undefined correlations — remove before running.\n","# Skill: Pairwise Correlation Analysis\n\n## Use When\n\n- User wants to scan for linear or monotonic relationships among multiple numeric variables\n- User needs a correlation matrix with p-values for gene expression, neural activity, or metabolomics data\n- User wants to identify highly correlated features before modeling (collinearity check)\n- User is building a co-expression or co-activity network\n\n## Inputs\n\n- Required:\n  - Data matrix (CSV or TSV): rows = observations, columns = numeric variables\n- Optional:\n  - Correlation method: `pearson` (default), `spearman`, or `kendall`\n  - Multiple-testing correction method: `fdr` (Benjamini-Hochberg, default) or `bonferroni`\n  - Significance threshold for highlighting (default: `0.05`)\n  - Output directory (default: `.\u002Fcorr_output`)\n\n## Workflow\n\n1. Read the data matrix; keep only numeric columns.\n2. Compute the full pairwise correlation matrix using the specified method.\n3. Compute p-values for each pair using the appropriate test (Pearson: t-distribution; Spearman\u002FKendall: permutation or asymptotic).\n4. Apply multiple-testing correction across all variable pairs.\n5. Generate a correlation heatmap: color-coded by correlation coefficient, asterisks for significant pairs (adjusted p \u003C threshold).\n6. Generate a scatter plot matrix (pairs plot) for datasets with ≤ 10 variables.\n7. Report the top 20 strongest positive and top 20 strongest negative correlations.\n8. Write correlation matrix (TSV), adjusted p-value matrix (TSV), and plots (PDF) to output directory.\n9. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n\n## Output Contract\n\n- Correlation matrix (TSV): variable × variable, values = correlation coefficients\n- Adjusted p-value matrix (TSV): variable × variable, values = corrected p-values\n- Correlation heatmap (PDF)\n- Pairs plot (PDF, only if ≤ 10 variables)\n- Top correlations summary table (TSV): var1, var2, correlation, p_value, adj_p_value\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Pearson correlation assumes linearity; use Spearman or Kendall for non-linear monotonic relationships or ordinal data.\n- Correlation does not imply causation.\n- Kendall's tau is slow for n > 10000; Spearman is preferred for large samples.\n- Common failure: constant or near-constant columns produce undefined correlations — remove before running.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fstat-pairwise-correlation",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 00:01:40",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-pairwise-correlation",{"tool":44,"command":45},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill stat-pairwise-correlation",{"tool":47,"command":48},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill 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