Machine learning

Multivariate statistics

Chemistry

Chromatography

Data warehouse

Artificial intelligence

Anthropology

Sample (material)

Pattern recognition (psychology)

Compositional data

Euclidean distance

Data mining

Computer science

Normalization (sociology)

Sociology

Data transformation

Database normalization

Bioinformatics

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.

Understanding sequencing data as compositions: an outlook and review