An efficient method to identify differentially expressed genes in microarray experiments.
Bioinformatics
; 24(14): 1583-9, 2008 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-18453554
ABSTRACT
MOTIVATION Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. RESULTS:
We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.AVAILABILITY:
The C++ code to implement the proposed method is available upon request for academic use.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
Base de dados:
MEDLINE
Assunto principal:
Regulação Neoplásica da Expressão Gênica
/
Biologia Computacional
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos