scholarly journals Understanding sequencing data as compositions: an outlook and review

2017 ◽  
Author(s):  
Thomas P. Quinn ◽  
Ionas Erb ◽  
Mark F. Richardson ◽  
Tamsyn M. Crowley
Keyword(s):  
Statistical Models ◽  
Compositional Data ◽  
Correlation Coefficients ◽  
Distance Measures ◽  
Compositional Data Analysis ◽  
Sequencing Data ◽  
Multivariate Statistical ◽  
Library Size ◽  
Future Directions ◽  
Sequencing Platforms

AbstractMotivationAlthough seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models.ResultsThe purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

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

2018 ◽  
Author(s):  
Thomas P. Quinn ◽  
Ionas Erb ◽  
Greg Gloor ◽  
Cedric Notredame ◽  
Mark F. Richardson ◽  
...  
Keyword(s):  
Data Analysis ◽  
General Solution ◽  
Compositional Data ◽  
Compositional Analysis ◽  
Compositional Data Analysis ◽  
Sequencing Data ◽  
Nucleotide Synthesis ◽  
Library Size ◽  
Concise Guide ◽  
Log Ratio

AbstractNext-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.

scholarly journals High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis

Biometrika ◽  
2021 ◽  
Author(s):  
Pixu Shi ◽  
Yuchen Zhou ◽  
Anru R Zhang
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Estimation Error ◽  
Real Data ◽  
Upper And Lower Bounds ◽  
High Dimensional ◽  
Compositional Data Analysis ◽  
Sequencing Data ◽  
Contrast Model ◽  
Critical Issues

Abstract In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. In this article, we introduce a surprisingly simple, interpretable, and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides both corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.

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

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Thomas P Quinn ◽  
Ionas Erb ◽  
Greg Gloor ◽  
Cedric Notredame ◽  
Mark F Richardson ◽  
...  
Keyword(s):  
Data Analysis ◽  
General Solution ◽  
Compositional Data ◽  
Compositional Analysis ◽  
Compositional Data Analysis ◽  
Nucleotide Synthesis ◽  
Library Size ◽  
Next Generation Sequencing Ngs ◽  
Concise Guide ◽  
Log Ratio

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?”

scholarly journals Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data

2016 ◽  
Vol 62 (8) ◽  
pp. 692-703 ◽  
Author(s):  
Gregory B. Gloor ◽  
Gregor Reid
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Compositional Data ◽  
Critical Role ◽  
Compositional Data Analysis ◽  
Data Sets ◽  
Clear Understanding ◽  
Sequencing Data ◽  
Microbiome Data

A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.

scholarly journals Log-ratio analysis of microbiome data with many zeroes is library size dependent

2020 ◽  
Author(s):  
Dennis te Beest ◽  
Els Nijhuis ◽  
Tim Mohlmann ◽  
Caro Ter braak
Keyword(s):  
Compositional Data ◽  
Explanatory Variable ◽  
Principal Component ◽  
Real Data ◽  
Amplicon Sequencing ◽  
Sequencing Data ◽  
Library Size ◽  
Sequencing Platform ◽  
Microbiome Composition ◽  
Log Ratio

Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artifact of the sequencing platform and as a result such data are compositional. To avoid library size dependency, one common way of analyzing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centered log-ratio, hereafter called a log-ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this paper we show on real data and by simulation that, applied to data that combines these two aspects, log-ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log-ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.

scholarly journals Compositional Data Analysis in Time-Use Epidemiology: What, Why, How

2020 ◽  
Vol 17 (7) ◽  
pp. 2220 ◽  
Author(s):  
Dorothea Dumuid ◽  
Željko Pedišić ◽  
Javier Palarea-Albaladejo ◽  
Josep Antoni Martín-Fernández ◽  
Karel Hron ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Sedentary Behavior ◽  
Time Use ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Health It ◽  
Future Directions ◽  
The Relationship ◽  
Daily Time

In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.

scholarly journals Why and How Should Geologists Use Compositional Data Analysis

2018 ◽  
Author(s):  
Ricardo A. Valls
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Special Property ◽  
Estimation Procedure ◽  
Real Space ◽  
Compositional Data Analysis ◽  
Constrained Estimation ◽  
Multivariate Statistical ◽  
Copper Mineralization ◽  
Normal Correlation

Compositional data arise naturally in several branches of science, including geology. In geochemistry, for example, these constrained data seem to occur typically, when one normalizes raw data or when one obtains the output from a constrained estimation procedure, such as parts per one, percentages, ppm, ppb, molar concentRations, etc. Compositional data have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. The special property of compositional data (the fact that the determinations on each specimen sum to a constant) means that the variables involved in the study occur in constrained space defined by the simplex, a restricted part of real space. Pearson was the first to point out dangers that may befall the analyst who attempts to interpret correlations between Ratios whose numerators and denominators contain common parts. More recently, Aitchison, Pawlowsky-Glahn, S. Thió, and other statisticians have develop the concept of Compositional Data Analysis, pointing out the dangers of misinterpretation of closed data when treated with “normal” statistical methods It is important for geochemists and geologists in general to be aware that the usual multivariate statistical techniques are not applicable to constrained data. It is also important for us to have access to appropriate techniques as they become available. This is the principal aim of this book. From a hypothetical model of a copper mineralization associated to a felsic intrusive, with specific relationships between certain elements, I will show how “normal” correlation methods fail to identify some of such embedded relationships and how we can obtain other spurious correlations. From there, I will test the same model after transforming the data using the CRL, ARL, and IRL transformations with the aid of the CoDaPack software. Since I addressed this publication to geologists and geoscientists in general, I have kept to a minimum the mathematical formulae and did not include any theoretical demonstRation. The “mathematical curios geologist”, if such category exists, can find all of those in a list of recommended sources in the reference section. So let us start by introducing the model of mineralization that we will be testing.

Compositional data analysis in epidemiology

2016 ◽  
Vol 27 (6) ◽  
pp. 1878-1891 ◽  
Author(s):  
Mehmet C Mert ◽  
Peter Filzmoser ◽  
Gottfried Endel ◽  
Ingrid Wilbacher
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Statistical Analyses ◽  
Analysis Approach ◽  
Compositional Data Analysis ◽  
Data Sets ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Data Set ◽  
Relative Information

Compositional data analysis refers to analyzing relative information, based on ratios between the variables in a data set. Data from epidemiology are usually treated as absolute information in an analysis. We outline the differences in both approaches for univariate and multivariate statistical analyses, using illustrative data sets from Austrian districts. Not only the results of the analyses can differ, but in particular the interpretation differs. It is demonstrated that the compositional data analysis approach leads to new and interesting insights.

Compositional Data Analysis in Tourism: Review and Future Directions

2020 ◽  
Vol 25 (1) ◽  
pp. 153-168 ◽  
Author(s):  
Germà Coenders ◽  
Berta Ferrer-Rosell
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Future Research ◽  
Compositional Data Analysis ◽  
Relative Importance ◽  
Future Directions ◽  
Tourist Satisfaction ◽  
Tourist Flows ◽  
Research Questions ◽  
Tourism And Hospitality

Compositional data analysis (CoDa) is the standard statistical methodology when data contain information about the relative importance of parts of a whole. Many research questions in tourism are either related to distribution of a whole (e.g., distribution, share, allocation, etc.) or relative importance (e.g., dominance, concentration, profile, etc.). Example research questions might be: How does time allocated to different types of activities relate to tourist satisfaction? or Which origins and destinations concentrate the most tourist flows per tourist segment? The first aim of this article is to present the manner in which CoDa solves statistical problems that arise when treating compositional data with classical statistical methods (e.g., spurious correlations, meaningless distances, assumption violation). The second aim is to review all CoDa applications in tourism and hospitality to date. The third is to present CoDa applications in related fields (e.g., finance, sociology, geography, economics, management, ecology, education), which can be translated into future research in tourism. In order to show how to apply the most common CoDa tools (exploratory analysis of compositions, and use of compositions as variables in a model) an example of restaurant menu styles is used.

Compositional Data Analysis

2021 ◽  
Vol 8 (1) ◽  
pp. 271-299
Author(s):  
Michael Greenacre
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Domain Knowledge ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Zero Values ◽  
Ratio Approach ◽  
Fundamental Transformation

Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1% or 100%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.

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