Name
Title | ||
|---|---|---|
Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA |
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. 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1093/bioinformatics/btm251 | Bioinformatics |
Keywords | Field | DocType |
gene expression,genetic transcription,microarray analysis,genetic selection,principal component analysis,gene expression profiling,bioinformatics,data base,microarray data,computer simulation,gene selection,biology,algorithms,analysis of variance,time series analysis,dimension reduction,statistical analysis,computational biology,mathematical analysis | Data mining,Dimensionality reduction,Microarray,Computer science,Microarray analysis techniques,Bioinformatics,Component analysis,Microarray databases,Spotting,Gene expression profiling,Principal component analysis | Journal |
Volume | Issue | ISSN |
23 | 14 | 1367-4803 |
Citations | PageRank | References |
8 | 1.05 | 8 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| María José Nueda | 1 | 101 | 6.56 |
| Ana Conesa | 2 | 364 | 24.48 |
| Johan A Westerhuis | 3 | 34 | 4.82 |
| Huub C J Hoefsloot | 4 | 78 | 12.80 |
| Age K Smilde | 5 | 176 | 16.49 |
| Manuel Talón | 6 | 162 | 10.94 |
| Alberto Ferrer | 7 | 80 | 4.78 |