Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets

Washburne, Alex D; Silverman, Justin D.; Leff, Jonathan W.; Bennett, Dominic J.; Darcy, John L.; Mukherjee, Sayan; Fierer, Noah; David, Lawrence A.

doi:10.7287/peerj.preprints.2685v1

Cited by 14 publications

(17 citation statements)

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“…When the basis is a branch of a dendrogram, the ilr offers an intuitive way to contrast one set of components against another set of components. These contrasts, called balances, have been used to analyze meta-genomics data based on evolutionary trees [57,67], but could be applied to any data if a similarly meaningful tree were available.…”

Section: Part 2a: Transformation-dependent Analysesmentioning

confidence: 99%

A field guide for the compositional analysis of any-omics data

Quinn

Erb

Gloor

et al. 2018

Preprint

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Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. Today, NGS is routinely used to understand many important topics in biology from human disease to microorganism diversity. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: the magnitude of the counts are 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 comparing heterogeneous samples (e.g., samples collected across distinct cancers or tissues). Instead, methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. In this manuscript, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. In doing so, we review zero replacement, differential abundance analysis, and within-group and between-group coordination analysis. We then discuss how this pipeline can accommodate complex study design, facilitate the analysis of vertically and horizontally integrated data, including multiomics data, and further extend to single-cell sequencing data. 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?". Taken together, this manuscript establishes the first fully comprehensive analysis protocol that is suitable for any and all -omics data.

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Section: Part 2a: Transformation-dependent Analysesmentioning

confidence: 99%

A field guide for the compositional analysis of any-omics data

Quinn

Erb

Gloor

et al. 2018

Preprint

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“…Null simulations may allow statistical statements stemming from a clear null model, but stopping criteria can be far more computationally efficient. Washburne et al (2017) proposed a stopping criterion for regression phylofactorization which extends to all methods of phylofactorization using an objective function whose null-distribution for a single edge is known. The original stopping criterion is based on the fact that, if the null hypothesis is true, the distribution of P values from multiple hypothesis tests is uniform.…”

Section: Statistical Challengesmentioning

confidence: 99%

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

Washburne

Silverman

Morton

et al. 2017

Preprint

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21The problem of pattern and scale is a central challenge in ecology. The problem 22 of scale is central to community ecology, where functional ecological groups are 23 aggregated and treated as a unit underlying an ecological pattern, such as ag-24 gregation of nitrogen xing trees into a total abundance of a trait underlying 25 ecosystem physiology. With the emergence of massive community ecological 26 datasets, from microbiomes to breeding bird surveys, there is a need to objec-27 tively identify the scales of organization pertaining to well-dened patterns in 28 community ecological data. 29The phylogeny is a scaold for identifying key phylogenetic scales associ-30 ated with macroscopic patterns. Phylofactorization was developed to objec-31 tively identify phylogenetic scales underlying patterns in relative abundance 32 data. However, many ecological data, such as presence-absences and counts, 33are not relative abundances, yet it is still desireable and informative to identify 34 phylogenetic scales underlying a pattern of interest. Here, we generalize phylo-35 factorization beyond relative abundances to a graph-partitioning algorithm for 36 any community ecological data. 37

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“…Although more complicated than a simple log-ratio, having more parts means that a single balance can describe more variance than a single log-ratio. Balances have recently become popular for the analysis and classification of microbiome compositions [44,46,29,40,37].…”

Section: Introductionmentioning

confidence: 99%

Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

Quinn

Erb

2020

Preprint

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In the health sciences, many data sets produced by next-generation sequencing (NGS) only contain relative information because of biological and technical factors that limit the total number of nucleotides observed for a given sample. As mutually dependent elements, it is not possible to interpret any component in isolation, at least without normalization. The field of compositional data analysis (CoDA) has emerged with alternative methods for relative data based on log-ratio transforms. However, NGS data often contain many more features than samples, and thus require creative new ways to reduce the dimensionality of the data without sacrificing interpretability. The summation of parts, called amalgamation, is a practical way of reducing dimensionality, but can introduce a non-linear distortion to the data. We exploit this non-linearity to propose a powerful yet interpretable dimension reduction method. In this report, we present data-driven amalgamation as a new method and conceptual framework for reducing the dimensionality of compositional data. Unlike expert-driven amalgamation which requires prior domain knowledge, our data-driven amalgamation method uses a genetic algorithm to answer the question, "What is the best way to amalgamate the data to achieve the user-defined objective?". We present a user-friendly R package, called amalgam, that can quickly find the optimal amalgamation to (a) preserve the distance between samples, or (b) classify samples as diseased or not. Our benchmark on 13 real data sets confirm that these amalgamations compete with the state-of-the-art unsupervised and supervised dimension reduction methods in terms of performance, but result in new variables that are much easier to understand: they are groups of features added together.

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Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets

Cited by 14 publications

References 0 publications

A field guide for the compositional analysis of any-omics data

A field guide for the compositional analysis of any-omics data

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

Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

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