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Understanding sequencing data as compositions: an outlook and review

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journal contribution
posted on 2018-08-15, 00:00 authored by Thomas Quinn, Ionas Erb, Mark RichardsonMark Richardson, Tamsyn CrowleyTamsyn Crowley
Motivation: Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models. Results: The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study. Supplementary information: Supplementary data are available at Bioinformatics online.

History

Journal

Bioinformatics

Volume

34

Issue

16

Pagination

2870 - 2878

Publisher

Oxford Academic

Location

London, Eng.

ISSN

1367-4803

eISSN

1367-4811

Language

eng

Publication classification

C1 Refereed article in a scholarly journal