A field guide for the compositional analysis of any-omics data
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- dc.contributor.author Quinn, Thomas P.
- dc.contributor.author Erb, Ionas
- dc.contributor.author Gloor, Greg
- dc.contributor.author Notredame, Cedric
- dc.contributor.author Richardson, Mark F.
- dc.contributor.author Crowley, Tamsyn M.
- dc.date.accessioned 2020-03-26T13:57:57Z
- dc.date.available 2020-03-26T13:57:57Z
- dc.date.issued 2019
- dc.description.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?"
- dc.format.mimetype application/pdf
- dc.identifier.citation Quinn TP, Erb I, Gloor G, Notredame C, Richardson MF, Crowley TM. A field guide for the compositional analysis of any-omics data. Gigascience. 2019; 8(9). pii:giz107. DOI: 10.1093/gigascience/giz107
- dc.identifier.doi http://dx.doi.org/10.1093/gigascience/giz107
- dc.identifier.issn 2047-217X
- dc.identifier.uri http://hdl.handle.net/10230/44056
- dc.language.iso eng
- dc.publisher Oxford University Press
- dc.relation.ispartof Gigascience. 2019; 8(9). pii:giz107
- dc.rights © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.title A field guide for the compositional analysis of any-omics data
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion