Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition

TL;DR

This paper introduces a new combinatorial model for identifying gene regulatory modules using weighted clique decomposition, demonstrating fixed parameter tractability and implementing algorithms tested on synthetic biological data.

Contribution

It proposes a novel weighted clique decomposition model for gene modules, with algorithms based on linear programming and integer partitioning, and validates them on synthetic data.

Findings

Algorithms are fixed parameter tractable when parameterized by the number of modules.

Implemented algorithms successfully identify modules in synthetic biological data.

Model captures complex gene interaction effects effectively.

Abstract

We present a new combinatorial model for identifying regulatory modules in gene co-expression data using a decomposition into weighted cliques. To capture complex interaction effects, we generalize the previously-studied weighted edge clique partition problem. As a first step, we restrict ourselves to the noise-free setting, and show that the problem is fixed parameter tractable when parameterized by the number of modules (cliques). We present two new algorithms for finding these decompositions, using linear programming and integer partitioning to determine the clique weights. Further, we implement these algorithms in Python and test them on a biologically-inspired synthetic corpus generated using real-world data from transcription factors and a latent variable analysis of co-expression in varying cell types.