{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T05:27:18Z","timestamp":1735968438768,"version":"3.32.0"},"reference-count":24,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>With the recent advance of sequencing technology, the collection of RNA expression (RNA\u2010seq) data has been growing rapidly. RNA\u2010seq data are statistically count\u2010type measurements. Poisson distribution is a basic probability distribution for modeling count\u2010type data. With Poisson regression models, various experimental factors, GC content as well as alternative splicing isoforms can be flexibly considered in RNA\u2010seq data analysis. Due to the biochemical and technical limitations of sequencing technology, the biases among RNA\u2010seq data have been recognized.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, an artificial censoring approach has been proposed to an isoform\u2010specific Poisson regression model for analyzing RNA\u2010seq data. Low expression values can be grouped (censored) into one probability category, and high expression values can also be grouped (censored) into another probability category. We have implemented the related Newton\u2010Raphson numeric computing procedure to achieve the maximum likelihood estimation for our censored\u2010Poisson regression model. The related mathematical simplifications have been derived for the consideration of stable and convenient numerical computing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The advantages of our artificial censoring approach have been demonstrated in both simulation studies and application analysis of experimental data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our proposed artificial censoring approach allows us to focus on the majority of data. As the extreme values (tails) of data are artificially censored, more efficient analysis results can be obtained, even from relatively simple Poisson regression models. Our proposed artificial censoring approach can certainly be considered for other well\u2010developed models or methods for RNA\u2010seq data analysis.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-020-0208-3","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T17:02:34Z","timestamp":1592240554000},"page":"155-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A censored\u2010Poisson model based approach to the analysis of RNA\u2010seq data"],"prefix":"10.1002","volume":"8","author":[{"given":"Xing","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Statistics The George Washington University Washington DC 20052 USA"}]},{"given":"Yinglei","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Statistics The George Washington University Washington DC 20052 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