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Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE).
Meng, Hailong; Yaari, Gur; Bolen, Christopher R; Avey, Stefan; Kleinstein, Steven H.
Afiliação
  • Meng H; Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Yaari G; Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.
  • Bolen CR; Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America.
  • Avey S; Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
  • Kleinstein SH; Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America.
PLoS Comput Biol ; 15(4): e1006899, 2019 04.
Article em En | MEDLINE | ID: mdl-30939133
ABSTRACT
Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Expressão Gênica / Perfilação da Expressão Gênica Tipo de estudo: Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Expressão Gênica / Perfilação da Expressão Gênica Tipo de estudo: Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article