Casual Compressive Sensing for Gene Network Inference

TL;DR

This paper introduces a new framework combining compressive sensing and Granger causality to infer gene interactions, successfully identifying causal relationships in E. coli gene expression data.

Contribution

It presents a novel approach that integrates compressive sensing with Granger causality for gene network inference, enabling detection of sparse causal dependencies.

Findings

Successfully identified known and unknown gene causal relationships in E. coli

Demonstrated effectiveness of the method on the Gardner dataset

Provided a new tool for causal inference in gene networks

Abstract

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered.