Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study

摘要

Motivation: Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package]. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic end-points (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data.