LRSA: A new computational method for analyzing time course microarray data

TL;DR

LRSA is a novel nonparametric method for analyzing time course microarray data, effectively identifying differentially expressed genes with lower false discovery rates compared to existing methods, validated by real-time PCR.

Contribution

Introduces LRSA, a two-step nonparametric approach that improves detection of differentially expressed genes in time course microarray data, especially with few replicates.

Findings

LRSA detects more differentially expressed genes than STEM and ANOVA.

LRSA achieves lower false discovery rates.

Results validated by real-time PCR.

Abstract

Motivation: Time course data obtained from biological samples subject to specific treatments can be very useful for revealing complex and novel biological phenomena. Although an increasing number of time course microarray datasets becomes available, most of them contain few biological replicates and time points. So far there are few computational methods that can effectively reveal differentially expressed genes and their patterns in such data. Results: We have proposed a new two-step nonparametric statistical procedure, LRSA, to reveal differentially expressed genes and their expression trends in temporal microarray data. We have also employed external controls as a surrogate to estimate false discovery rates and thus to guide the discovery of differentially expressed genes. Our results showed that LRSA reveals substantially more differentially expressed genes and have much lower than two other methods, STEM and ANOVA, in both real data and the simulated data. Our computational results are confirmed using real-time PCRs. Contact: wuw2@upmc.edu