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However, metabolite data typically contain a large proportion of missing values, which may result from either low abundance or technical challenges in data processing. Moreover, given the compositionality of microbiome data, where the observed abundances can only be interpreted on a relative scale, standard variable selection methods are not applicable. In this project, we propose a novel Bayesian regression method to address these challenges in the integration of metabolite and microbiome data. Key features of our proposed model include modeling the two different mechanisms of missingness for the metabolite data and adopting a Bayesian prior designed to address the compositional characteristics of microbiome data. We demonstrate on simulated data that our proposed model can accurately impute the unobserved true metabolite values and correctly select the relevant microbiome predictors. We further illustrate our method using real data from a study focused on understanding the interplay between the microbiome and metabolome in colorectal cancer.<\/jats:p>","DOI":"10.1002\/sim.70607","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T05:42:09Z","timestamp":1779774129000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Sparse Regression for Microbiome\u2013Metabolite Data Integration"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9825-058X","authenticated-orcid":false,"given":"Kai","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Data Science The University of Texas Health Science Center at Houston  Houston Texas USA"},{"name":"Department of Biostatistics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satabdi","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Biostatistics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine B.","family":"Peterson","sequence":"additional","affiliation":[{"name":"Department of Statistics Rice University  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-022-00974-4"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/microorganisms7010014"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2014.03.001"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31502-1"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0884533615609896"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.hsr.2021.100010"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.4110\/in.2023.23.e6"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20215296"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-010814-020351"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.1c04093"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1515\/sagmb-2013-0021"},{"key":"e_1_2_11_13_1","volume-title":"Statistical Analysis With Missing Data","author":"Rubin D. 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