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Phylofactorization: a graph-partitioning algorithm to identify phylogenetic scales of ecological data

View ORCID ProfileAlex D. Washburne, View ORCID ProfileJustin D. Silverman, James T. Morton, View ORCID ProfileDaniel J. Becker, Daniel Crowley, Sayan Mukherjee, Lawrence A. David, Raina K. Plowright
doi: https://doi.org/10.1101/235341
Alex D. Washburne
1Department of Microbiology and Immunology, Montana State University, Bozeman MT, 59717, USA
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Justin D. Silverman
2Program for Computational Biology and Bioinformatics, Duke University, Durham NC, 27708, USA
3Center for Genomic and Computational Biology, Duke University, Durham NC, 27708, USA
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James T. Morton
4Department of Computer Science, University of California San Diego, La Jolla CA, 92037, USA
5Department of Pediatrics, University of California San Diego, La Jolla CA, 92037, USA
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Daniel J. Becker
1Department of Microbiology and Immunology, Montana State University, Bozeman MT, 59717, USA
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Daniel Crowley
1Department of Microbiology and Immunology, Montana State University, Bozeman MT, 59717, USA
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Sayan Mukherjee
3Center for Genomic and Computational Biology, Duke University, Durham NC, 27708, USA
6Department of Statistical Science, Mathematics, and Computer Science, Duke University, Durham NC, 27708 USA
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Lawrence A. David
3Center for Genomic and Computational Biology, Duke University, Durham NC, 27708, USA
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Raina K. Plowright
1Department of Microbiology and Immunology, Montana State University, Bozeman MT, 59717, USA
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Abstract

The problem of pattern and scale is a central challenge in ecology. The problem of scale is central to community ecology, where functional ecological groups are aggregated and treated as a unit underlying an ecological pattern, such as aggregation of “nitrogen fixing trees” into a total abundance of a trait underlying ecosystem physiology. With the emergence of massive community ecological datasets, from microbiomes to breeding bird surveys, there is a need to objectively identify the scales of organization pertaining to well-defined patterns in community ecological data.

The phylogeny is a scaffold for identifying key phylogenetic scales associated with macroscopic patterns. Phylofactorization was developed to objectively identify phylogenetic scales underlying patterns in relative abundance data. However, many ecological data, such as presence-absences and counts, are not relative abundances, yet it is still desireable and informative to identify phylogenetic scales underlying a pattern of interest. Here, we generalize phylofactorization beyond relative abundances to a graph-partitioning algorithm for any community ecological data.

Generalizing phylofactorization connects many tools from data analysis to phylogenetically-informe analysis of community ecological data. Two-sample tests identify three phylogenetic factors of mammalian body mass which arose during the K-Pg extinction event, consistent with other analyses of mammalian body mass evolution. Projection of data onto coordinates defined by the phylogeny yield a phylogenetic principal components analysis which refines our understanding of the major sources of variation in the human gut microbiome. These same coordinates allow generalized additive modeling of microbes in Central Park soils and confirm that a large clade of Acidobacteria thrive in neutral soils. Generalized linear and additive modeling of exponential family random variables can be performed by phylogenetically-constrained reduced-rank regression or stepwise factor contrasts. We finish with a discussion of how phylofac-torization produces an ecological species concept with a phylogenetic constraint. All of these tools can be implemented with a new R package available online.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 03, 2018.
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Phylofactorization: a graph-partitioning algorithm to identify phylogenetic scales of ecological data
Alex D. Washburne, Justin D. Silverman, James T. Morton, Daniel J. Becker, Daniel Crowley, Sayan Mukherjee, Lawrence A. David, Raina K. Plowright
bioRxiv 235341; doi: https://doi.org/10.1101/235341
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Phylofactorization: a graph-partitioning algorithm to identify phylogenetic scales of ecological data
Alex D. Washburne, Justin D. Silverman, James T. Morton, Daniel J. Becker, Daniel Crowley, Sayan Mukherjee, Lawrence A. David, Raina K. Plowright
bioRxiv 235341; doi: https://doi.org/10.1101/235341

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