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Published (Version of record)CC BY-NC V4.0, Open Access
Published (Version of record)CC BY-NC V4.0, Open
Journal article
Understanding sequencing data as compositions: an outlook and review
Bioinformatics, Vol.34(16), pp.2870-2878
15/08/2018
PMID: 29608657
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Abstract
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.
Details
- Title
- Understanding sequencing data as compositions: an outlook and review
- Creators
- Thomas P Quinn - Deakin University (Australia, Geelong)Ionas Erb - Centre for Genomic RegulationMark F Richardson - Deakin University (Australia, Geelong)Tamsyn M Crowley - Deakin University (Australia, Geelong)
- Publication Details
- Bioinformatics, Vol.34(16), pp.2870-2878
- Publisher
- Oxford University Press
- Number of pages
- 9
- Identifiers
- 991013328727802368
- Copyright
- © The Author(s) 2018.
- Academic Unit
- Office of Senior Deputy Vice Chancellor, Research
- Language
- English
- Resource Type
- Journal article