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

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 unwant...

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Vydáno v:Bioinformatics Ročník 23; číslo 14; s. 1792 - 1800
Hlavní autoři: Nueda, María José, Conesa, Ana, Westerhuis, Johan A., Hoefsloot, Huub C. J., Smilde, Age K., Talón, Manuel, Ferrer, Alberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Oxford University Press 15.07.2007
Oxford Publishing Limited (England)
Témata:
Algorithms
Analysis of Variance
Bioinformatics
Biological and medical sciences
Computational Biology - methods
Computer Simulation
Data Interpretation, Statistical
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis - methods
Principal Component Analysis
Time Factors
Transcription, Genetic
Variance analysis
Transcription
Noise
DNA chip
Synthetic test
Gene expression
Microarray
Variance analysis
Response
Original document
Signal
Computer program
Environment
Extraction
Bioinformatics
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí: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. Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Contact: mj.nueda@ua.es and aconesa@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online.
Bibliografie:istex:CA5736B827166095A49CDA2BEAA8B83C05111F4B
To whom correspondence should be addressed.
ark:/67375/HXZ-B90DS8MS-7
Associate Editor: Joaquin Dopazo
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btm251