NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

Abstract

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.

SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

“…We searched for the densest connected subnetworks of 6 different sizes (10, 15, 20, 25, 30, and 35 vertices) from a dual network of 176,839 physical interaction edges from HINT+HI network 114 - a combination of HINT and HI interaction networks - and 561 genetic dependency edges derived from SuperDendrix associations for 511 differential dependencies. We computed a P -value for each subnetwork using a permutation test by permuting genetic dependency edges as described in a previous study.…”

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks

“…Biological network-based cancer gene prediction methods have been extensively studied in recent years. At present, there exists a series of methods designed to detect cancer gene modules with mutational features ( Reyna et al 2018 , 2021 , Chitra et al 2022 ). EMOGI ( Schulte-Sasse et al 2021 ) integrates multi-omics data for multiple cancers and protein–protein interaction (PPI) networks via graph convolutional networks (GCNs) ( Kipf and Welling 2017 ) to learn local feature patterns of cancer genes.…”