Lineage-Based Identification of Cellular States and Expression Programs

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Lineage-Based Identification of Cellular States and Expression Programs

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dc.contributor.author Jaakkola, Tommi
dc.contributor.author Mazzoni, Esteban O.
dc.contributor.author Wichterle, Hynek
dc.contributor.author Hashimoto, Tatsunori Benjamin
dc.contributor.author Sherwood, Richard Irving
dc.contributor.author Gifford, David Kenneth
dc.date.accessioned 2013-03-15T19:30:20Z
dc.date.issued 2012
dc.identifier.citation Hashimoto, Tatsunori, Tommi Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, and David Gifford. 2012. Lineage-based identification of cellular states and expression programs. Bioinformatics 28(12): i250-i257. en_US
dc.identifier.issn 1367-4803 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:10419412
dc.description.abstract Summary: We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L1 that based methods controls the parameters in three distinct ways: the number of genes change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization, techniques, such as singular value decomposition and non-negative matrix factorization show that our method provides higher predictive power in held, out tests while inducing sparse and biologically relevant gene sets. en_US
dc.language.iso en_US en_US
dc.publisher Oxford University Press en_US
dc.relation.isversionof doi:10.1093/bioinformatics/bts204 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371836/pdf/ en_US
dash.license LAA
dc.subject Gene Regulation and Transcriptomics en_US
dc.title Lineage-Based Identification of Cellular States and Expression Programs en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Bioinformatics en_US
dash.depositing.author Hashimoto, Tatsunori Benjamin
dc.date.available 2013-03-15T19:30:20Z

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