Sparse group factor analysis for biclustering of multiple data sources.
Bioinformatics
; 32(16): 2457-63, 2016 08 15.
Article
em En
| MEDLINE
| ID: mdl-27153643
ABSTRACT
MOTIVATION Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expression data. We present a Bayesian approach for joint biclustering of multiple data sources, extending a recent method Group Factor Analysis to have a biclustering interpretation with additional sparsity assumptions. The resulting method enables data-driven detection of linear structure present in parts of the data sources. RESULTS:
Our simulation studies show that the proposed method reliably infers biclusters from heterogeneous data sources. We tested the method on data from the NCI-DREAM drug sensitivity prediction challenge, resulting in an excellent prediction accuracy. Moreover, the predictions are based on several biclusters which provide insight into the data sources, in this case on gene expression, DNA methylation, protein abundance, exome sequence, functional connectivity fingerprints and drug sensitivity. AVAILABILITY AND IMPLEMENTATION http//research.cs.aalto.fi/pml/software/GFAsparse/ CONTACTS kerstin.bunte@googlemail.com or samuel.kaski@aalto.fi.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Análise por Conglomerados
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Perfilação da Expressão Gênica
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Conjuntos de Dados como Assunto
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article