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


BACKGROUND
ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Nueda
María José
MJ
Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es
Conesa
Ana
A
Westerhuis
Johan A
JA
Hoefsloot
Huub C J
HC
Smilde
Age K
AK
Talón
Manuel
M
Ferrer
Alberto
A
D000465:Algorithms; D000704:Analysis of Variance; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D013997:Time Factors; D014158:Transcription, Genetic