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To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivorship bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation\u2013maximization (EM) algorithm, which we call lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>An implementation of LEM is available at https:\/\/github.com\/so-nakashima\/Lineage-EM-algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa040","type":"journal-article","created":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T08:50:12Z","timestamp":1579164612000},"page":"2829-2838","source":"Crossref","is-referenced-by-count":18,"title":["Lineage EM algorithm for inferring latent states from cellular lineage trees"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5287-7475","authenticated-orcid":false,"given":"So","family":"Nakashima","sequence":"first","affiliation":[{"name":"Department of Mathematical Informatics, Graduate School of Information Science and Technology"}]},{"given":"Yuki","family":"Sughiyama","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo , Tokyo 113-8654, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8474-7942","authenticated-orcid":false,"given":"Tetsuya J","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Department of Mathematical Informatics, Graduate School of Information Science and Technology"},{"name":"Institute of Industrial Science, The University of Tokyo , Tokyo 113-8654, Japan"},{"name":"PRESTO, Japan Science and Technology Agency (JST) , Saitama 332-0012, Japan"}]}],"member":"286","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"key":"2023013111494123100_btaa040-B1","first-page":"199","volume-title":"Information Theory and an Extension of the Maximum Likelihood Principle","author":"Akaike","year":"1998"},{"key":"2023013111494123100_btaa040-B2","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.gde.2011.10.001","article-title":"Persistence: mechanisms for triggering and enhancing phenotypic variability","volume":"21","author":"Balaban","year":"2011","journal-title":"Curr. 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