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mbImpute: an accurate and robust imputation method for microbiome data

by Li, Jingyi Jessica , Li, Wei Vivian , Jiang, Ruochen

in Actinobacteria - classification / Actinobacteria - genetics / Actinobacteria - isolation & purification / Animal Genetics and Genomics / Bacteroidetes - classification / Bacteroidetes - genetics / Bacteroidetes - isolation & purification / Bioinformatics / Biomedical and Life Sciences / Case-Control Studies / Colorectal cancer / colorectal neoplasms / Colorectal Neoplasms - diagnosis / Colorectal Neoplasms - microbiology / Colorectal Neoplasms - pathology / Data analysis / Diabetes mellitus (non-insulin dependent) / Diabetes Mellitus, Type 2 - diagnosis / Diabetes Mellitus, Type 2 - microbiology / Diabetes Mellitus, Type 2 - pathology / Disease / DNA, Bacterial - genetics / Evolutionary Biology / Firmicutes - classification / Firmicutes - genetics / Firmicutes - isolation & purification / Fusobacteria - classification / Fusobacteria - genetics / Fusobacteria - isolation & purification / Gene expression / Generalized linear models / genome / Genomes / Human Genetics / Humans / Life Sciences / metadata / Metagenome / Method / Methods / Microbial Genetics and Genomics / microbiome / Microbiomes / Microbiota - genetics / noninsulin-dependent diabetes mellitus / Phylogeny / Plant Genetics and Genomics / Polymerase Chain Reaction - methods / Proteobacteria - classification / Proteobacteria - genetics / Proteobacteria - isolation & purification / RNA, Ribosomal, 16S - genetics / Software / Sparsity / Whole Genome Sequencing

2021

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mbImpute: an accurate and robust imputation method for microbiome data

by Li, Jingyi Jessica , Li, Wei Vivian , Jiang, Ruochen

2021

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mbImpute: an accurate and robust imputation method for microbiome data

by Li, Jingyi Jessica , Li, Wei Vivian , Jiang, Ruochen

2021

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Journal Article

mbImpute: an accurate and robust imputation method for microbiome data

Li, Jingyi Jessica,

Li, Wei Vivian,

Jiang, Ruochen

2021

Overview

A 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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Publisher

BioMed Central,Springer Nature B.V,BMC

Subject

Actinobacteria - classification

/ Actinobacteria - genetics

/ Actinobacteria - isolation & purification

/ Animal Genetics and Genomics

/ Bacteroidetes - classification

/ Bacteroidetes - genetics

/ Bacteroidetes - isolation & purification