Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Sudhakar Jonnalagadda,Rajagopalan Srinivasan

doi:10.1186/1471-2105-9-267

Sudhakar Jonnalagadda, Rajagopalan Srinivasan

Open Access

https://doi.org/10.1186/1471-2105-9-267

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Journal: BMC Bioinformatics	Publication Date: Jun 6, 2008
Citations: 54	License type: CC BY 2.0

Affiliation: National University of Singapore

Abstract

BackgroundTime-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.ResultsIn this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C1 and C2). We model the expression at C1 using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C2 is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.ConclusionIn both cases, the proposed method identified biologically significant genes.

Highlights

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes
Trinklein et al [20] analyzed the transcriptional response of different gene groups: (A) mouse genes homologues of human genes that are bound by heat-shock transcription factor 1 (HSF1) and induced, (B) homologues that were bound by HSF1 but not induced, (C) homologues that were induced but not bound by HSF1, (D) genes induced by heat in wild-type but not in mutant, (E) genes induced in mutant mouse, (F) genes induced in both wildtype and mutant
Genes belonging to groups A, D and E should be identified as differentially expressed between wild-type and HSF1 mutant mouse and genes belonging to groups C and F as expressed

Summary

Introduction

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. Snapshots of gene expression levels are measured in two different cell populations, such as normal and diseased [2]. Measuring expression levels irrespective of time does not provide information about the dynamic interactions that characterize the cellular processes [3]. This necessitates time-course experiments where gene expression levels are measured at different timepoints and across biological conditions such as wild-type and gene-knockout strains [4] or normal and stimulated cells [5]

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Results

Discussion

Conclusion