Lineage-based identification of cellular states and expression programs
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 improv...
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| Format: | Article |
| Language: | en_US |
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Oxford University Press
2012
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| Online Access: | http://hdl.handle.net/1721.1/75412 https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-2199-0379 https://orcid.org/0000-0003-1709-4034 |
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| author | Hashimoto, Tatsunori Benjamin Jaakkola, Tommi S. Sherwood, Richard Mazzoni, Esteban O. Wichterle, Hynek Gifford, David K. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Hashimoto, Tatsunori Benjamin Jaakkola, Tommi S. Sherwood, Richard Mazzoni, Esteban O. Wichterle, Hynek Gifford, David K. |
| author_sort | Hashimoto, Tatsunori Benjamin |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:55:29Z |
| format | Article |
| id | mit-1721.1/75412 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:55:29Z |
| publishDate | 2012 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/754122022-09-30T12:11:39Z Lineage-based identification of cellular states and expression programs Hashimoto, Tatsunori Benjamin Jaakkola, Tommi S. Sherwood, Richard Mazzoni, Esteban O. Wichterle, Hynek Gifford, David K. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Hashimoto, Tatsunori Benjamin Jaakkola, Tommi S. Gifford, David K. 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. National Institutes of Health (U.S.) (P01-NS055923) National Institutes of Health (U.S.) (1-UL1-RR024920) 2012-12-12T16:46:14Z 2012-12-12T16:46:14Z 2012-01 Article http://purl.org/eprint/type/JournalArticle 1367-4803 1460-2059 http://hdl.handle.net/1721.1/75412 Hashimoto, T. et al. “Lineage-based Identification of Cellular States and Expression Programs.” Bioinformatics 28.12 (2012): i250–i257. https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-2199-0379 https://orcid.org/0000-0003-1709-4034 en_US http://dx.doi.org/10.1093/bioinformatics/bts204 Bioinformatics Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/3.0 application/pdf Oxford University Press Oxford |
| spellingShingle | Hashimoto, Tatsunori Benjamin Jaakkola, Tommi S. Sherwood, Richard Mazzoni, Esteban O. Wichterle, Hynek Gifford, David K. 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 |
| url | http://hdl.handle.net/1721.1/75412 https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-2199-0379 https://orcid.org/0000-0003-1709-4034 |
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