RT Journal Article
SR Electronic
T1 Understanding sequencing data as compositions: an outlook and review
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 206425
DO 10.1101/206425
A1 Thomas P. Quinn
A1 Ionas Erb
A1 Mark F. Richardson
A1 Tamsyn M. Crowley
YR 2017
UL http://biorxiv.org/content/early/2017/10/19/206425.abstract
AB 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 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.