{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T08:15:27Z","timestamp":1775981727339,"version":"3.50.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,3,5]],"date-time":"2020-03-05T00:00:00Z","timestamp":1583366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R15GM131390"],"award-info":[{"award-number":["R15GM131390"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>R code available at https:\/\/github.com\/S-YIN\/MIXnorm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Contact<\/jats:title><jats:p>swang@smu.edu<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa153","type":"journal-article","created":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T12:24:10Z","timestamp":1582979050000},"page":"3401-3408","source":"Crossref","is-referenced-by-count":6,"title":["MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples"],"prefix":"10.1093","volume":"36","author":[{"given":"Shen","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Statistical Science , Southern Methodist University, Dallas, TX 75275-0332, USA"},{"name":"Department of Population and Data Sciences , Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8561-6511","authenticated-orcid":false,"given":"Xinlei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistical Science , Southern Methodist University, Dallas, TX 75275-0332, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoxiang","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Statistical Science , Southern Methodist University, Dallas, TX 75275-0332, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences , Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,3,5]]},"reference":[{"key":"2023062312015726600_btaa153-B1","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023062312015726600_btaa153-B2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/1471-2105-11-94","article-title":"Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments","volume":"11","author":"Bullard","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023062312015726600_btaa153-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. 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