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Lineage-based identification of cellular states and expression programs

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 significant...

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Autores principales: Hashimoto, Tatsunori, Jaakkola, Tommi, Sherwood, Richard, Mazzoni, Esteban O., Wichterle, Hynek, Gifford, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371836/
https://www.ncbi.nlm.nih.gov/pubmed/22689769
http://dx.doi.org/10.1093/bioinformatics/bts204
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author Hashimoto, Tatsunori
Jaakkola, Tommi
Sherwood, Richard
Mazzoni, Esteban O.
Wichterle, Hynek
Gifford, David
author_facet Hashimoto, Tatsunori
Jaakkola, Tommi
Sherwood, Richard
Mazzoni, Esteban O.
Wichterle, Hynek
Gifford, David
author_sort Hashimoto, Tatsunori
collection PubMed
description 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 L(1) 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. Contact: gifford@mit.edu
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spelling pubmed-33718362012-06-11 Lineage-based identification of cellular states and expression programs Hashimoto, Tatsunori Jaakkola, Tommi Sherwood, Richard Mazzoni, Esteban O. Wichterle, Hynek Gifford, David Bioinformatics Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa 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 L(1) 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. Contact: gifford@mit.edu Oxford University Press 2012-06-15 2012-06-09 /pmc/articles/PMC3371836/ /pubmed/22689769 http://dx.doi.org/10.1093/bioinformatics/bts204 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa
Hashimoto, Tatsunori
Jaakkola, Tommi
Sherwood, Richard
Mazzoni, Esteban O.
Wichterle, Hynek
Gifford, David
Lineage-based identification of cellular states and expression programs
title Lineage-based identification of cellular states and expression programs
title_full Lineage-based identification of cellular states and expression programs
title_fullStr Lineage-based identification of cellular states and expression programs
title_full_unstemmed Lineage-based identification of cellular states and expression programs
title_short Lineage-based identification of cellular states and expression programs
title_sort lineage-based identification of cellular states and expression programs
topic Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371836/
https://www.ncbi.nlm.nih.gov/pubmed/22689769
http://dx.doi.org/10.1093/bioinformatics/bts204
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