DMCA
Graphical methods for class prediction using dimension reduction techniques on DNA microarray data (2003)
| Venue: | Bioinformatics |
| Citations: | 31 - 2 self |
BibTeX
@ARTICLE{Bura03graphicalmethods,
author = {Efstathia Bura and Ruth M. Pfeiffer},
title = {Graphical methods for class prediction using dimension reduction techniques on DNA microarray data},
journal = {Bioinformatics},
year = {2003}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Motivation: We introduce simple graphical classification and prediction tools for tumour status using gene-expression profiles. They are based on two dimension estimation techniques sliced aver-age variance estimation (SAVE) and sliced inverse regression (SIR). Both SAVE and SIR are used to infer on the dimension of the classification problem and obtain linear combinations of genes that contain sufficient information to predict class membership, such as tumour type. Plots of the estimated directions as well as numerical thresholds estimated from the plots are used to predict tumour classes in cDNA microarrays and the performance of the class predictors is assessed by cross-validation. A microarray simulation study is carried out to compare the power and predictive accuracy of the two methods. Results: The methods are applied to cDNA microarray data on BRCA1 and BRCA2 mutation carriers as well as sporadic tumours from Hedenfalk et al. (2001). All samples are correctly classi-fied. Contact:
Keyphrases
class prediction graphical method dimension reduction technique dna microarray data cdna microarrays tumour status sporadic tumour simple graphical classification sufficient information sliced inverse regression class predictor microarray data numerical threshold dimension estimation technique gene-expression profile class membership aver-age variance estimation predictive accuracy prediction tool linear combination classification problem estimated direction tumour type brca2 mutation carrier microarray simulation study tumour class
