publication . Preprint . 2020

mbImpute: an accurate and robust imputation method for microbiome data

Ruochen Jiang; Wei Vivian Li; Jingyi Jessica Li;
Open Access English
  • Published: 08 Mar 2020
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>Microbiome 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 v...
Subjects
free text keywords: Covariate, Computational biology, Microbiome, Metadata, Imputation (statistics), Computer science, Human microbiome
Funded by
NIH| Robust Identification and accurate quantification of RNA transcripts on a system wide scale
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01GM120507-01
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
,
NSF| CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1846216
  • Funding stream: Directorate for Biological Sciences | Division of Biological Infrastructure
36 references, page 1 of 3

[7] Hongzhe Li. Microbiome, metagenomics, and high-dimensional compositional data analysis. Annual Review of Statistics and Its Application, 2:73-94, 2015. [OpenAIRE]

[8] Juan Jovel, Jordan Patterson, Weiwei Wang, Naomi Hotte, Sandra O'Keefe, Troy Mitchel, Troy Perry, Dina Kao, Andrew L Mason, Karen L Madsen, et al. Characterization of the gut microbiome using 16s or shotgun metagenomics. Frontiers in microbiology, 7:459, 2016.

[9] Georg Zeller, Julien Tap, Anita Y Voigt, Shinichi Sunagawa, Jens Roat Kultima, Paul I Costea, Aure´lien Amiot, Ju¨rgen Bo¨hm, Francesco Brunetti, Nina Habermann, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Molecular systems biology, 10(11), 2014.

[10] Qiang Feng, Suisha Liang, Huijue Jia, Andreas Stadlmayr, Longqing Tang, Zhou Lan, Dongya Zhang, Huihua Xia, Xiaoying Xu, Zhuye Jie, et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nature communications, 6:6528, 2015.

[11] Jun Yu, Qiang Feng, Sunny Hei Wong, Dongya Zhang, Qiao yi Liang, Youwen Qin, Longqing Tang, Hui Zhao, Jan Stenvang, Yanli Li, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut, 66(1):70-78, 2017.

[12] Emily Vogtmann, Xing Hua, Georg Zeller, Shinichi Sunagawa, Anita Y Voigt, Rajna Hercog, James J Goedert, Jianxin Shi, Peer Bork, and Rashmi Sinha. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PloS one, 11(5), 2016.

[13] Fredrik H Karlsson, Valentina Tremaroli, Intawat Nookaew, Go¨ran Bergstro¨m, Carl Johan Behre, Bjo¨rn Fagerberg, Jens Nielsen, and Fredrik Ba¨ckhed. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature, 498(7452):99-103, 2013.

[14] Junjie Qin, Yingrui Li, Zhiming Cai, Shenghui Li, Jianfeng Zhu, Fan Zhang, Suisha Liang, Wenwei Zhang, Yuanlin Guan, Dongqian Shen, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490(7418):55-60, 2012.

[15] Kirsten JM van Nimwegen, Ronald A van Soest, Joris A Veltman, Marcel R Nelen, Gert Jan van der Wilt, Lisenka ELM Vissers, and Janneke PC Grutters. Is the 1000 genome as near as we think? a cost analysis of next-generation sequencing. Clinical chemistry, 62(11):1458- 1464, 2016.

[16] Fan Xia, Jun Chen, Wing Kam Fung, and Hongzhe Li. A logistic normal multinomial regression model for microbiome compositional data analysis. Biometrics, 69(4):1053-1063, 2013.

[17] Siddhartha Mandal, Will Van Treuren, Richard A White, Merete Eggesbø, Rob Knight, and Shyamal D Peddada. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease, 26(1):27663, 2015.

[18] Abhishek Kaul, Siddhartha Mandal, Ori Davidov, and Shyamal D Peddada. Analysis of microbiome data in the presence of excess zeros. Frontiers in microbiology, 8:2114, 2017.

[19] Lizhen Xu, Andrew D Paterson, Williams Turpin, and Wei Xu. Assessment and selection of competing models for zero-inflated microbiome data. PloS one, 10(7), 2015.

[20] Jun Chen, Emily King, Rebecca Deek, Zhi Wei, Yue Yu, Diane Grill, and Karla Ballman. An omnibus test for differential distribution analysis of microbiome sequencing data. Bioinformatics, 34(4):643-651, 2018.

[21] M Claire Horner-Devine, Jessica M Silver, Mathew A Leibold, Brendan JM Bohannan, Robert K Colwell, Jed A Fuhrman, Jessica L Green, Cheryl R Kuske, Jennifer BH Martiny, Gerard Muyzer, et al. A comparison of taxon co-occurrence patterns for macro-and microorganisms. Ecology, 88(6):1345-1353, 2007.

36 references, page 1 of 3
Abstract
<jats:title>Abstract</jats:title><jats:p>Microbiome 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 v...
Subjects
free text keywords: Covariate, Computational biology, Microbiome, Metadata, Imputation (statistics), Computer science, Human microbiome
Funded by
NIH| Robust Identification and accurate quantification of RNA transcripts on a system wide scale
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01GM120507-01
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
,
NSF| CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1846216
  • Funding stream: Directorate for Biological Sciences | Division of Biological Infrastructure
36 references, page 1 of 3

[7] Hongzhe Li. Microbiome, metagenomics, and high-dimensional compositional data analysis. Annual Review of Statistics and Its Application, 2:73-94, 2015. [OpenAIRE]

[8] Juan Jovel, Jordan Patterson, Weiwei Wang, Naomi Hotte, Sandra O'Keefe, Troy Mitchel, Troy Perry, Dina Kao, Andrew L Mason, Karen L Madsen, et al. Characterization of the gut microbiome using 16s or shotgun metagenomics. Frontiers in microbiology, 7:459, 2016.

[9] Georg Zeller, Julien Tap, Anita Y Voigt, Shinichi Sunagawa, Jens Roat Kultima, Paul I Costea, Aure´lien Amiot, Ju¨rgen Bo¨hm, Francesco Brunetti, Nina Habermann, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Molecular systems biology, 10(11), 2014.

[10] Qiang Feng, Suisha Liang, Huijue Jia, Andreas Stadlmayr, Longqing Tang, Zhou Lan, Dongya Zhang, Huihua Xia, Xiaoying Xu, Zhuye Jie, et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nature communications, 6:6528, 2015.

[11] Jun Yu, Qiang Feng, Sunny Hei Wong, Dongya Zhang, Qiao yi Liang, Youwen Qin, Longqing Tang, Hui Zhao, Jan Stenvang, Yanli Li, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut, 66(1):70-78, 2017.

[12] Emily Vogtmann, Xing Hua, Georg Zeller, Shinichi Sunagawa, Anita Y Voigt, Rajna Hercog, James J Goedert, Jianxin Shi, Peer Bork, and Rashmi Sinha. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PloS one, 11(5), 2016.

[13] Fredrik H Karlsson, Valentina Tremaroli, Intawat Nookaew, Go¨ran Bergstro¨m, Carl Johan Behre, Bjo¨rn Fagerberg, Jens Nielsen, and Fredrik Ba¨ckhed. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature, 498(7452):99-103, 2013.

[14] Junjie Qin, Yingrui Li, Zhiming Cai, Shenghui Li, Jianfeng Zhu, Fan Zhang, Suisha Liang, Wenwei Zhang, Yuanlin Guan, Dongqian Shen, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490(7418):55-60, 2012.

[15] Kirsten JM van Nimwegen, Ronald A van Soest, Joris A Veltman, Marcel R Nelen, Gert Jan van der Wilt, Lisenka ELM Vissers, and Janneke PC Grutters. Is the 1000 genome as near as we think? a cost analysis of next-generation sequencing. Clinical chemistry, 62(11):1458- 1464, 2016.

[16] Fan Xia, Jun Chen, Wing Kam Fung, and Hongzhe Li. A logistic normal multinomial regression model for microbiome compositional data analysis. Biometrics, 69(4):1053-1063, 2013.

[17] Siddhartha Mandal, Will Van Treuren, Richard A White, Merete Eggesbø, Rob Knight, and Shyamal D Peddada. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease, 26(1):27663, 2015.

[18] Abhishek Kaul, Siddhartha Mandal, Ori Davidov, and Shyamal D Peddada. Analysis of microbiome data in the presence of excess zeros. Frontiers in microbiology, 8:2114, 2017.

[19] Lizhen Xu, Andrew D Paterson, Williams Turpin, and Wei Xu. Assessment and selection of competing models for zero-inflated microbiome data. PloS one, 10(7), 2015.

[20] Jun Chen, Emily King, Rebecca Deek, Zhi Wei, Yue Yu, Diane Grill, and Karla Ballman. An omnibus test for differential distribution analysis of microbiome sequencing data. Bioinformatics, 34(4):643-651, 2018.

[21] M Claire Horner-Devine, Jessica M Silver, Mathew A Leibold, Brendan JM Bohannan, Robert K Colwell, Jed A Fuhrman, Jessica L Green, Cheryl R Kuske, Jennifer BH Martiny, Gerard Muyzer, et al. A comparison of taxon co-occurrence patterns for macro-and microorganisms. Ecology, 88(6):1345-1353, 2007.

36 references, page 1 of 3
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