Title
Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA
Author
Nueda, M.J.
Conesa, A.
Westerhuis, J.A.
Hoefsloot, H.C.J.
Smilde, A.K.
Talón, M.
Ferrer, A.
TNO Kwaliteit van Leven
Publication year
2007
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).
Subject
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
To reference this document use:
http://resolver.tudelft.nl/uuid:41bd9410-7067-42fa-a695-91e37db75745
DOI
https://doi.org/10.1093/bioinformatics/btm251
TNO identifier
240090
ISSN
1367-4803
Source
Bioinformatics, 23 (14), 1792-1800
Document type
article