Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.
Nat Methods
; 13(7): 577-80, 2016 07.
Article
en En
| MEDLINE
| ID: mdl-27240256
ABSTRACT
Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (http//www.bioconductor.org/packages/IHW). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Genoma Humano
/
Interpretación Estadística de Datos
/
Perfilación de la Expresión Génica
/
Genómica
/
Modelos Teóricos
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2016
Tipo del documento:
Article
País de afiliación:
Alemania