Analyzing microarray data with transitive directed acyclic graphs.
J Bioinform Comput Biol
; 7(1): 135-56, 2009 Feb.
Article
em En
| MEDLINE
| ID: mdl-19226664
ABSTRACT
Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
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Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
Tipo de estudo:
Evaluation_studies
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article