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However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feature. A kernel-smoothed likelihood method is developed to estimate model coefficients and a likelihood ratio test is constructed for differential abundant analysis. The method has been implemented into an R package, <jats:italic>SDAMS<\/jats:italic>, which is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.bioconductor.org\/packages\/release\/bioc\/html\/SDAMS.html\">https:\/\/www.bioconductor.org\/packages\/release\/bioc\/html\/SDAMS.html<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>By introducing the two-part semi-parametric model, SDA is able to handle both non-normally distributed data and large fraction of zero values in a MS dataset. It also allows for adjustment of covariates. Simulations and real data analyses demonstrate that SDA outperforms existing methods.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3067-z","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T14:59:24Z","timestamp":1571324364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data"],"prefix":"10.1186","volume":"20","author":[{"given":"Yuntong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teresa W.M.","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew N.","family":"Lane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woo-Young","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susanne M.","family":"Arnold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnold J.","family":"Stromberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8984-4995","authenticated-orcid":false,"given":"Chi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,17]]},"reference":[{"issue":"11","key":"3067_CR1","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1002\/cbic.200500151","volume":"6","author":"EJ Want","year":"2005","unstructured":"Want EJ, Cravatt BF, Siuzdak G. 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