A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments
Jung Klaus (),
Kramer Frank and
Additional contact information
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
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)
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:15:y:2016:i:5:p:401-414:n:3
Ordering information: This journal article can be ordered from
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().