Listen to genes: dealing with microarray data in the frequency domain.

Jianfeng Feng,Shuixia Guo,Dongyun Yi,Ritesh Krishna,Vicky Buchanan-Wollaston

doi:10.1371/journal.pone.0005098

Abstract

BackgroundWe present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.MethodologyGenes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.ConclusionsWe use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.

Highlights

Uncovering the biological meaning embedded in time-series gene expression data is one of the most challenging problems in the post genomic era
Similar microarray study on circadian rhythm in Arabidopsis was carried out by Harmen et al.[5] which empirically tested for statistically significant cross-correlation between temporal profile of each gene and cosine wave of definite period and phase
Complex Granger causality: Network Analyzing We have developed a novel approach called Partial Granger causality (PGC) to analyze network structures in genes, neurons and brain areas[16]

Summary

Introduction

Uncovering the biological meaning embedded in time-series gene expression data is one of the most challenging problems in the post genomic era. In comparison with fixed single time point microarray data, the expression patterns observed over multiple time periods provide us with a rich set of information detailing the temporal profiles of the genes. Such profiles when studied at the genome wide level can help us fully understand the underlying cellular processes and facilitate the development of potential therapeutic targets. Similar microarray study on circadian rhythm in Arabidopsis was carried out by Harmen et al.[5] which empirically tested for statistically significant cross-correlation between temporal profile of each gene and cosine wave of definite period and phase. The approach includes normalization, clustering and network analysis of genes

Methods

Results

Discussion

Conclusion