Reference : Testing for association between RNA-Seq and high-dimensional data
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Engineering, computing & technology : Computer science
Computational Sciences; Systems Biomedicine
http://hdl.handle.net/10993/41428
Testing for association between RNA-Seq and high-dimensional data
English
Rauschenberger, Armin mailto [VU University Medical Center, Amsterdam, The Netherlands > Department of Epidemiology and Biostatistics]
Jonker, Marianne A. mailto [VU University Medical Center, Amsterdam, The Netherlands > Department of Epidemiology and Biostatistics]
van de Wiel, Mark A. mailto [VU University Medical Center, Amsterdam, The Netherlands > Department of Epidemiology and Biostatistics > > ; VU University, Amsterdam, The Netherlands > Department of Mathematics]
Menezes, Renée X. mailto [VU University Medical Center, Amsterdam, The Netherlands > Department of Epidemiology and Biostatistics]
8-Mar-2016
BMC Bioinformatics
BioMed Central
17
118
Yes (verified by ORBilu)
International
1471-2105
London
United Kingdom
[en] high-dimensional ; overdispersion ; negative binomial ; global test ; integration ; RNA-seq
[en] Background: Testing for association between RNA-Seq and other genomic data is challenging due to high variability of the former and high dimensionality of the latter. Results: Using the negative binomial distribution and a random-effects model, we develop an omnibus test that overcomes both difficulties. It may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Conclusions: The proposed test can detect genetic and epigenetic alterations that affect gene expression. It can examine complex regulatory mechanisms of gene expression. The R package globalSeq is available from Bioconductor.
Department of Epidemiology and Biostatistics, VU University Medical Center
Researchers
http://hdl.handle.net/10993/41428
10.1186/s12859-016-0961-5
https://doi.org/10.1186/s12859-016-0961-5
The original publication is available from BMC Bioinformatics (open access).
https://bioconductor.org/packages/globalSeq/

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