Statistical significance of variables driving systematic variation in high-dimensional data.
Bioinformatics
; 31(4): 545-54, 2015 Feb 15.
Article
em En
| MEDLINE
| ID: mdl-25336500
ABSTRACT
MOTIVATION There are a number of well-established methods such as principal component analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (PCs) (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting. RESULTS:
We introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of PCs. The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be used to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify genes that are cell-cycle regulated with an accurate measure of statistical significance. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly driven phenotype. Using our method, we find a greater enrichment for inflammatory-related gene sets compared to the original analysis that uses a clinically defined, although likely imprecise, phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses. AVAILABILITY AND IMPLEMENTATION An R software package, called jackstraw, is available in CRAN. CONTACT jstorey@princeton.edu.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Software
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Interpretação Estatística de Dados
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Modelos Estatísticos
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Genômica
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Análise de Componente Principal
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Inflamação
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Estados Unidos