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A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments

Kruppa Jochen, Jung Klaus (), Kramer Frank and Beißbarth Tim
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Jung Klaus: Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Foundation, Hannover, D-30559, Germany
Beißbarth Tim: Department of Medical Statistics, University Medical Center Göttingen, 37099 Göttingen, Germany

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 5, 401-414

Abstract: As part of the data processing of high-throughput-sequencing experiments count data are produced representing the amount of reads that map to specific genomic regions. Count data also arise in mass spectrometric experiments for the detection of protein-protein interactions. For evaluating new computational methods for the analysis of sequencing count data or spectral count data from proteomics experiments artificial count data is thus required. Although, some methods for the generation of artificial sequencing count data have been proposed, all of them simulate single sequencing runs, omitting thus the correlation structure between the individual genomic features, or they are limited to specific structures. We propose to draw correlated data from the multivariate normal distribution and round these continuous data in order to obtain discrete counts. In our approach, the required distribution parameters can either be constructed in different ways or estimated from real count data. Because rounding affects the correlation structure we evaluate the use of shrinkage estimators that have already been used in the context of artificial expression data from DNA microarrays. Our approach turned out to be useful for the simulation of counts for defined subsets of features such as individual pathways or GO categories.

Keywords: count data; gene ontology; next-generation sequencing; pathways; simulation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2015-0082

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