Differential meta-analysis of RNA-seq data ...
Type de document :
Article dans une revue scientifique
DOI :
PMID :
URL permanente :
Titre :
Differential meta-analysis of RNA-seq data from multiple studies
Auteur(s) :
Rau, Andrea [Auteur]
Marot, Guillemette [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Jaffrézic, Florence [Auteur]
Marot, Guillemette [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Jaffrézic, Florence [Auteur]
Titre de la revue :
BMC Bioinformatics
Nom court de la revue :
BMC Bioinformatics
Numéro :
15
Pagination :
91
Éditeur :
BioMed Central
Date de publication :
2014
ISSN :
1471-2105
Discipline(s) HAL :
Sciences du Vivant [q-bio]/Biochimie, Biologie Moléculaire
Résumé en anglais : [en]
Background High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates ...
Lire la suite >Background High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. Results We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. Conclusions The p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http:\/\/cran.r-project.org\/web\/packages\/metaRNASeq).Lire moins >
Lire la suite >Background High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. Results We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. Conclusions The p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http:\/\/cran.r-project.org\/web\/packages\/metaRNASeq).Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:31Z
2020-06-09T08:33:57Z
2020-06-09T08:33:57Z
Fichiers
- documen
- Accès libre
- Accéder au document