Multi-Context Colocalization Analysis for QTL and GWAS Studies
Plot visualization plot from a ColocBoost output.
Validate and Process All Input Data for ColocBoost
ColocBoost: A gradient boosting informed multi-omics xQTL colocalizati...
Get ambiguous colocalization events from trait-specific (uncolocalized...
Get summary tables from a ColocBoost output.
A fast function to calculate correlation matrix (LD matrix) from indiv...
Calculate purity within and in-between CoS
Get colocalization summary table from a ColocBoost output.
Extract CoS at different coverage
Perform modularity-based hierarchical clustering for a correlation mat...
Recalibrate and summarize robust colocalization events.
Get trait-specific summary table from a ColocBoost output.
A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.
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