Thomas P Quinn1,2, Ionas Erb3, Greg Gloor4, Cedric Notredame3, Mark F Richardson1,5,6, Tamsyn M Crowley7. 1. Bioinformatics Core Research Group, Deakin University, 1 Gheringhap Street, Geelong Victoria 3220, Australia. 2. Centre for Molecular and Medical Research, Deakin University, 1 Gheringhap Street, Geelong Victoria 3220, Australia. 3. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, Barcelona 08003, Spain. 4. Department of Biochemistry, University of Western Ontario, 1151 Richmond Street, London ON N6A 3K7, Canada. 5. Genomics Centre, School of Life and Environmental Sciences, Deakin University, 1 Gheringhap Street, Geelong Victoria 3220, Australia. 6. Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, 1 Gheringhap Street, Geelong Victoria 3220, Australia. 7. Poultry Hub Australia, University of New England, Elm Avenue, Armidale New South Wales 2351, Australia.
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?"
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?"
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