mbImpute: an accurate and robust imputation method for microbiome data

AbstractA critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data—mbImpute—to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

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mbImpute: an accurate and robust imputation method for microbiome data

10.1101/2020.03.07.982314 ◽

2020 ◽

Author(s):

Ruochen Jiang ◽

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

State Of The Art ◽

Imputation Accuracy ◽

Human Microbiome ◽

Real Data ◽

Imputation Method ◽

Imputation Methods ◽

Microbiome Research ◽

Microbiome Data ◽

Critical Challenge

AbstractMicrobiome studies have gained increased attention since many discoveries revealed connections between human microbiome compositions and diseases. A critical challenge in microbiome research is that excess non-biological zeros distort taxon abundances, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method, mbImpute, to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. Comprehensive simulations verified that mbImpute achieved better imputation accuracy under multiple measures than five state-of-the-art imputation methods designed for non-microbiome data. In real data applications, we demonstrate that mbImpute improved the power and reproducibility of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer.

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Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽

10.3390/molecules26051393 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1393

Author(s):

Ralitsa Robeva ◽

Miroslava Nedyalkova ◽

Georgi Kirilov ◽

Atanaska Elenkova ◽

Sabina Zacharieva ◽

...

Keyword(s):

Type 2 Diabetes ◽

Metabolic Syndrome ◽

Data Analysis ◽

Stress Response ◽

Exploratory Data Analysis ◽

Principal Component ◽

Hierarchical Cluster ◽

Obese Women ◽

Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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