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Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA

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Author: Nueda, M.J. · Conesa, A. · Westerhuis, J.A. · Hoefsloot, H.C.J. · Smilde, A.K. · Talón, M. · Ferrer, A.
Type:article
Date:2007
Institution: TNO Kwaliteit van Leven
Source:Bioinformatics, 14, 23, 1792-1800
Identifier: 240090
doi: doi:10.1093/bioinformatics/btm251
Keywords: Biology · Analytical research · analysis of variance · article · bioinformatics · correlation analysis · data base · gene expression profiling · genetic selection · genetic transcription · mathematical analysis · microarray analysis · nonhuman · priority journal · simultaneous component analysis · statistical analysis · time series analysis · Algorithms · Analysis of Variance · Computational Biology · Computer Simulation · Data Interpretation, Statistical · Gene Expression Profiling · Models, Genetic · Models, Statistical · Oligonucleotide Array Sequence Analysis · Principal Component Analysis · Time Factors · Transcription, Genetic

Abstract

Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. Results: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. © 2007 The Author(s).