metacp

metacp combines independent and dependent p-values using various statistical methods for meta-analyses and bioinformatics applications.

Key Features:

• Independent p-value combination: Implements Fisher's, Stouffer's and Edgington's methods for combining independent p-values.
• Dependent p-value combination: Implements Brown's method and the Cauchy Combination Test for combining dependent p-values.
• Multiple implementations: Available as a standalone Python tool and as a STATA command.
• Diverse analytical flexibility: Offers the largest collection of statistical methods for p-value combination compared to similar packages.

Scientific Applications:

• Gene-based testing: Can be used for gene-based testing depending on the input data.
• Multiple trait GWAS analysis: Supports analysis of multiple traits in genome-wide association studies.
• Multi-omics data integration: Enables combining diverse multi-omics data from TWAS, colocalization, or RNA-seq studies.
• Meta-analysis: Serves practitioners performing meta-analyses across various fields.
• Bioinformatics tool development: Supports bioinformaticians developing tools where p-value combination is a crucial component.

Methodology:

metacp implements statistical methods for combining p-values, including Fisher's, Stouffer's, and Edgington's methods for independent p-values, and Brown's method and the Cauchy Combination Test for dependent p-values.