{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T05:13:33Z","timestamp":1716268413744},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Metabolomics has been shown as an effective tool to study various biological and biomedical phenotypes, whereas interrogating the inherently noisy metabolite concentration data with limited sample size remains a major challenge. Accumulating evidence suggests that metabolites\u2019 structures are relevant to their bioactivities.<\/jats:p><jats:p>Results: We present a new strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. The strategy selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The effectiveness of the proposed strategy is demonstrated by both simulation studies and a real application. In a metabolomic study on Alzheimer\u2019s disease, the posterior inclusion probability for summary molecular descriptors reaches 0.97. By incorporating the structure data, our approach uniquely identifies multiple Alzheimer\u2019s disease signatures, which are consistent with existing evidence. These results evidently suggest the value of the proposed approach for metabolomic hypothesis-testing problems.<\/jats:p><jats:p>Availability and implementation: A code package implementing the strategy is freely available at https:\/\/github.com\/HongjieZhu\/CIMA.git.<\/jats:p><jats:p>Contact: \u00a0hongjie.zhu@sanofi.com<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt708","type":"journal-article","created":{"date-parts":[[2013,12,7]],"date-time":"2013-12-07T01:29:20Z","timestamp":1386379760000},"page":"514-522","source":"Crossref","is-referenced-by-count":2,"title":["Chemical structure informing statistical hypothesis testing in metabolomics"],"prefix":"10.1093","volume":"30","author":[{"given":"Hongjie","family":"Zhu","sequence":"first","affiliation":[{"name":"1 Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, 2Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, 3Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and 4Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA"},{"name":"1 Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, 2Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, 3Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and 4Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Luo","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, 2Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, 3Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and 4Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA"},{"name":"1 Department of Biostatistics and Programming, Sanofi, Bridgewater, NJ 08807, USA, 2Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA, 3Exploratory Clinical & Translational Research, Bristol-Myers Squibb, Princeton, NJ 08543, USA and 4Center for Human Health Assessment, The Hamner Institutes for Health Sciences, Durham, NC 27709, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,12,5]]},"reference":[{"key":"2023012710422821800_btt708-B1","doi-asserted-by":"crossref","DOI":"10.1002\/0471249688","volume-title":"Categorical Data Analysis","author":"Agresti","year":"2002","edition":"2nd edn"},{"key":"2023012710422821800_btt708-B2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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