Detection of multiple perturbations in multi-omics biological networks

TL;DR

This paper introduces a statistical method to identify multiple perturbation sources in multi-omics biological networks by extending network filtering techniques, demonstrated on cancer data.

Contribution

It develops a novel approach combining joint Gaussian graphical models and likelihood ratio tests for multi-perturbation detection in multi-omics data.

Findings

Successfully identified perturbation sites in TCGA data

Extended network filtering to handle multiple perturbations

Validated method's effectiveness on real biological datasets

Abstract

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA).