IPCAPS

IPCAPS performs iterative pruning Principal Component Analysis (ipPCA) to resolve fine-scale population genetic structure from SNP data.

Key Features:

• ipPCA framework: Employs an iterative pruning Principal Component Analysis (ipPCA) framework to improve resolution over traditional PCA-based methods.
• Outlier detection and removal: Iteratively detects and eliminates outliers to minimize misclassification errors.
• Subgroup assignment: Systematically assigns individuals to genetically similar subgroups based on PCA-derived structure.
• Fine-scale resolution: Targets closely related or geographically confined populations to resolve subtle population structure using SNP data.
• Multiple data types: Supports analysis of SNP datasets and can accommodate panels of gene expression and methylation data.
• Measurement scales: Accommodates various measurement scales for variables used to identify substructure.
• Implementation: Implemented as an R package.

Scientific Applications:

• Population structure analysis: Describes shared genetic ancestry and fine-scale structure within complex populations.
• Genetic variation studies: Enables detailed analysis of genetic variation using SNP data.
• Patient sub-phenotyping: Applies to patient sub-phenotyping using gene expression or methylation panels.

Methodology:

Implements an iterative pruning Principal Component Analysis (ipPCA) framework that assigns individuals to genetically similar subgroups while iteratively detecting and removing outliers.