VTwins: inferring causative microbial features from metagenomic data of limited samples

Qingren Meng; Qian Zhou; Shuo Shi; Jingfa Xiao; Qin Ma; Jun Yu; Jun Chen; Yu Kang

doi:10.1016/j.scib.2023.10.024

VTwins: inferring causative microbial features from metagenomic data of limited samples

Sci Bull (Beijing). 2023 Nov 30;68(22):2806-2816. doi: 10.1016/j.scib.2023.10.024. Epub 2023 Oct 28.

Authors

Qingren Meng¹, Qian Zhou², Shuo Shi³, Jingfa Xiao³, Qin Ma⁴, Jun Yu⁵, Jun Chen⁶, Yu Kang⁷

Affiliations

¹ CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518100, China.
² International Cancer Center, Shenzhen University Medical School, Shenzhen 518055, China.
³ CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China.
⁴ Department of Biomedical Informatics, The Ohio State University, Columbus OH 43210, USA.
⁵ CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China.
⁶ National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518100, China.
⁷ CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China. Electronic address: kangy@big.ac.cn.

PMID: 37919157
DOI: 10.1016/j.scib.2023.10.024

Abstract

It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method-virtual twins (VTwins)-to eliminate the confounder effects by transforming the original cohort into a paired cohort of "Twin" samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.

Keywords: Causality; Differential abundance; High-dimensional data; Metagenome; Paired cohort.

MeSH terms

Algorithms*
Humans
Metagenome* / genetics
Metagenomics / methods
Software

Abstract

MeSH terms

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