{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:50Z","timestamp":1772138090566,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,9,9]],"date-time":"2018-09-09T00:00:00Z","timestamp":1536451200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012390","name":"Swiss Initiative in Systems Biology","doi-asserted-by":"crossref","award":["RTD 2013\/152"],"award-info":[{"award-number":["RTD 2013\/152"]}],"id":[{"id":"10.13039\/501100012390","id-type":"DOI","asserted-by":"crossref"}]},{"name":"TargetInfectX \u2013 Multi-Pronged Perturbation of Pathogen Infection in Human Cells"},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a mixture of Nested Effects Models (M&amp;NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The mixture Nested Effects Model (M&amp;NEM) is available as the R-package mnem at https:\/\/github.com\/cbg-ethz\/mnem\/.<\/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\/bty602","type":"journal-article","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T02:00:37Z","timestamp":1530928837000},"page":"i964-i971","source":"Crossref","is-referenced-by-count":11,"title":["Single cell network analysis with a mixture of Nested Effects Models"],"prefix":"10.1093","volume":"34","author":[{"given":"Martin","family":"Pirkl","sequence":"first","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics, Basel, Switzerland"}]},{"given":"Niko","family":"Beerenwinkel","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics, Basel, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061313504559800_bty602-B1","doi-asserted-by":"crossref","first-page":"6447","DOI":"10.1073\/pnas.0809822106","article-title":"Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models","volume":"106","author":"Anchang","year":"2009","journal-title":"Proc. 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