Learning LiNGAM based on data with more variables than observations

TL;DR

This paper introduces a novel algorithm for identifying gene regulatory networks using LiNGAM, addressing the challenge of causal discovery from steady-state data in systems biology.

Contribution

It presents the first fully identifiable LiNGAM-based method for learning gene networks from steady-state data, overcoming limitations of Bayesian network approaches.

Findings

The proposed algorithm can uniquely identify causal gene networks.

Compared to existing methods, it shows improved accuracy on artificial data.

Potential for application to real microarray gene expression data.

Abstract

A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory networks with no prior knowledge of causal connectivity. Many methods have been developed for time series data. However, discovery methods based on steady-state data are often necessary and preferable since obtaining time series data can be more expensive and/or infeasible for many biological systems. A conventional approach is causal Bayesian networks. However, estimation of Bayesian networks is ill-posed. In many cases it cannot uniquely identify the underlying causal network and only gives a large class of equivalent causal networks that cannot be distinguished between based on the data distribution. We propose a new discovery algorithm for uniquely identifying the underlying causal network of genes. To the best of our knowledge, the proposed method is the first algorithm for learning gene networks based on a fully identifiable causal model called LiNGAM. We here compare our algorithm with competing algorithms using artificially-generated data, although it is definitely better to test it based on real microarray gene expression data.