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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
A field guide for the compositional analysis of any-omics data
Gigascience, Vol.8(9), pp.1-14
09/2019
PMID: 31544212
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Abstract
Background: Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: their magnitude is determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when heterogeneous samples are compared.
Results: Methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. Herein, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data.
Conclusions: In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, "Relative to some important activity of the cell, what is changing?"
Details
- Title
- A field guide for the compositional analysis of any-omics data
- Creators
- Thomas P. Quinn - Deakin University (Australia, Geelong)Ionas Erb - The Barcelona Institute of Science and Technology (Spain, Barcelona)Greg Gloor - University of Western OntarioCedric Notredame - Centre for Genomic RegulationMark F. Richardson - Deakin University (Australia, Geelong)Tamsyn M. Crowley - University of New England
- Publication Details
- Gigascience, Vol.8(9), pp.1-14
- Publisher
- Oxford University Press
- Number of pages
- 14
- Identifiers
- 991013328527702368
- Copyright
- © The Author(s) 2019.
- Academic Unit
- Office of Senior Deputy Vice Chancellor, Research
- Language
- English
- Resource Type
- Journal article