Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways

TL;DR

This paper introduces a novel logistic regression-based community detection method that integrates auxiliary gene information to identify autism-related gene pathways more accurately and robustly.

Contribution

It develops a joint pseudo-likelihood approach with an EM algorithm for gene relevance and clustering, including a robust version for complex linkage patterns, with proven consistency.

Findings

Outperforms existing methods in simulations

Successfully identifies autism-related gene sets

Demonstrates robustness across diverse data types

Abstract

When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the disease are unknown. We propose method to single out irrelevant background genes with the help of auxiliary information through a logistic regression, and cluster relevant genes into cohesive groups using the adjacency matrix. Expectation-maximization algorithm is modified to maximize a joint pseudo-likelihood assuming latent indicators for relevance to the disease and latent group memberships as well as Poisson or multinomial distributed link numbers within and between groups. A robust version allowing arbitrary linkage patterns within the background is further derived. Asymptotic consistency of label assignments under the stochastic blockmodel is proven. Superior performance and robustness in finite samples are observed in simulation studies. The proposed robust method identifies previously missed gene sets underlying autism related neurological diseases using diverse data sources including de novo mutations, gene expressions and protein-protein interactions.