Integrative analysis of gene expression and phenotype data

TL;DR

This paper presents three innovative methods for integrating gene expression and phenotype data, including automated phenotype profiling, robust gene subset selection for classification, and network module discovery, advancing understanding of complex traits like cancer.

Contribution

It introduces new approaches for phenotype profiling, gene subset selection, and network module identification, addressing challenges in high-dimensional, small-sample data analysis.

Findings

Robust multi-dimensional phenotype profiling using gene expression similarity.

Improved classification accuracy with sequences of discriminative gene clusters.

Identification of cancer subtype-specific gene network modules, including a potential tumor suppressor.

Abstract

The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and laborious process. We propose a method of automated multi-dimensional profiling which uses gene expression similarity. Large-scale analysis show that our method can provide robust profiling that reveals different phenotypic aspects of samples. This profiling technique is also capable of interpolation and extrapolation beyond the phenotype information given in training data. It can be used in many applications, including facilitating experimental design and detecting confounding factors. 2) Phenotype association analysis problems are complicated by small sample size and high dimensionality. Consequently, phenotype-associated gene subsets obtained from training data are very sensitive to selection of training samples, and the constructed sample phenotype classifiers tend to have poor generalization properties. To eliminate these obstacles, we propose a novel approach that generates sequences of increasingly discriminative gene cluster combinations. Our experiments on both simulated and real datasets show robust and accurate classification performance. 3) Many complex phenotypes, such as cancer, are the product of not only gene expression, but also gene interaction. We propose an integrative approach to find gene network modules that activate under different phenotype conditions. Using our method, we discovered cancer subtype-specific network modules, as well as the ways in which these modules coordinate. In particular, we detected a breast-cancer specific tumor suppressor network module with a hub gene, PDGFRL, which may play an important role in this module.