Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting

TL;DR

This paper introduces a robust nonparametric integrative analysis method using sparse boosting to identify heterogeneity and commonality across subgroups in biomedical data, accommodating nonlinear effects and data contamination.

Contribution

It develops a novel robust, nonparametric, sparse boosting approach that handles data contamination and nonlinear effects for integrative subgroup analysis.

Findings

Effective in simulations for heterogeneous data

Identifies biologically meaningful patterns in cancer data

Provides satisfactory prediction performance

Abstract

In many biomedical problems, data are often heterogeneous, with samples spanning multiple patient subgroups, where different subgroups may have different disease subtypes, stages, or other medical contexts. These subgroups may be related, but they are also expected to have differences with respect to the underlying biology. The heterogeneous data presents a precious opportunity to explore the heterogeneities and commonalities between related subgroups. Unfortunately, effective statistical analysis methods are still lacking. Recently, several novel methods based on integrative analysis have been proposed to tackle this challenging problem. Despite promising results, the existing studies are still limited by ignoring data contamination and making strict assumptions of linear effects of covariates on response. As such, we develop a robust nonparametric integrative analysis approach to identify heterogeneity and commonality, as well as select important covariates and estimate covariate effects. Possible data contamination is accommodated by adopting the Cauchy loss function, and a nonparametric model is built to accommodate nonlinear effects. The proposed approach is based on a sparse boosting technique. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on glioblastoma multiforme and lung adenocarcinoma shows that the proposed approach makes biologically meaningful findings with satisfactory prediction.