Beta approximation of ratio distribution and its application to next generation sequencing read counts

Shengping Yang and Zhide Fang

Journal of Applied Statistics, 2017, vol. 44, issue 1, 57-70

Abstract: Paired sequencing data are commonly collected in genomic studies to control biological variation. However, existing data processing strategies suffer at low coverage regions, which are unavoidable due to the limitation of current sequencing technology. Furthermore, information contained in the absolute values of the read counts is commonly ignored. We propose a read count ratio processing/modification method, to not only incorporate information contained in the absolute values of paired counts into one variable, but also mitigate the discrete artifact, especially when both counts are small. Simulation shows that the processed variable fits well with a Beta distribution, thus providing an easy tool for down-stream inference analysis.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2016.1158798 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:44:y:2017:i:1:p:57-70

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2016.1158798

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().