scholarly journals Robust covariance estimation for high‐dimensional compositional data with application to microbial communities analysis

2021 ◽  
Author(s):  
Yong He ◽  
Pengfei Liu ◽  
Xinsheng Zhang ◽  
Wang Zhou
Keyword(s):  
Microbial Communities ◽  
Compositional Data ◽  
Covariance Estimation ◽  
High Dimensional

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 Two-sample tests of high-dimensional means for compositional data

Biometrika ◽  
2017 ◽  
Vol 105 (1) ◽  
pp. 115-132 ◽  
Author(s):  
Yuanpei Cao ◽  
Wei Lin ◽  
Hongzhe Li
Keyword(s):  
Compositional Data ◽  
High Dimensional

scholarly journals Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation

2021 ◽  
Vol 12 ◽  
Author(s):  
Michael Greenacre ◽  
Marina Martínez-Álvaro ◽  
Agustín Blasco
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
High Dimensional ◽  
Compositional Data Analysis ◽  
Rna Transcripts ◽  
Operational Taxonomic Units ◽  
Logratio Transformation ◽  
Large Numbers ◽  
Fixed Reference ◽  
Reference Component

Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc.). These data are generally regarded as compositional since the total number of counts identified within a sample is irrelevant. The central concept in compositional data analysis is the logratio transformation, the simplest being the additive logratios with respect to a fixed reference component. A full set of additive logratios is not isometric, that is they do not reproduce the geometry of all pairwise logratios exactly, but their lack of isometry can be measured by the Procrustes correlation. The reference component can be chosen to maximize the Procrustes correlation between the additive logratio geometry and the exact logratio geometry, and for high-dimensional data there are many potential references. As a secondary criterion, minimizing the variance of the reference component's log-transformed relative abundance values makes the subsequent interpretation of the logratios even easier. On each of three high-dimensional omics datasets the additive logratio transformation was performed, using references that were identified according to the abovementioned criteria. For each dataset the compositional data structure was successfully reproduced, that is the additive logratios were very close to being isometric. The Procrustes correlations achieved for these datasets were 0.9991, 0.9974, and 0.9902, respectively. We thus demonstrate, for high-dimensional compositional data, that additive logratios can provide a valid choice as transformed variables, which (a) are subcompositionally coherent, (b) explain 100% of the total logratio variance and (c) come measurably very close to being isometric. The interpretation of additive logratios is much simpler than the complex isometric alternatives and, when the variance of the log-transformed reference is very low, it is even simpler since each additive logratio can be identified with a corresponding compositional component.

Compositional data network analysis via lasso penalized D-trace loss

2019 ◽  
Vol 35 (18) ◽  
pp. 3404-3411 ◽  
Author(s):  
Huili Yuan ◽  
Shun He ◽  
Minghua Deng
Keyword(s):  
Microbial Communities ◽  
Compositional Data ◽  
Sparse Matrix ◽  
Mouse Skin ◽  
Direct Interaction ◽  
Interaction Network ◽  
Gene Profiling ◽  
Supplementary Information ◽  
Rrna Gene ◽  
Skin Microbiome

Abstract Motivation With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality. Results In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data. Availability and implementation The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3. Supplementary information Supplementary data are available at Bioinformatics online.

Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

2017 ◽  
Vol 45 (11) ◽  
pp. 2067-2080 ◽  
Author(s):  
Jiajia Chen ◽  
Xiaoqin Zhang ◽  
Karel Hron ◽  
Matthias Templ ◽  
Shengjia Li
Keyword(s):  
Compositional Data ◽  
High Dimensional ◽  
Regression Imputation
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