Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
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- Eugenio Marco
- Department of Biostatistics and Computational Biology, Dana–Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
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- Robert L. Karp
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115;
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- Guoji Guo
- Division of Pediatric Hematology/Oncology, Boston Children’s Hospital and Dana–Farber Cancer Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02115;
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- Paul Robson
- Department of Biological Sciences, National University of Singapore and Genome Institute of Singapore, Singapore 138672; and
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- Adam H. Hart
- Department of Genetics, La Trobe University, Melbourne, VIC 3086, Australia
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- Lorenzo Trippa
- Department of Biostatistics and Computational Biology, Dana–Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
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- Guo-Cheng Yuan
- Department of Biostatistics and Computational Biology, Dana–Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
書誌事項
- 公開日
- 2014-12-15
- 権利情報
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- http://www.pnas.org/site/misc/userlicense.xhtml
- DOI
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- 10.1073/pnas.1408993111
- 公開者
- Proceedings of the National Academy of Sciences
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説明
<jats:title>Significance</jats:title> <jats:p>Characterization of cellular heterogeneity and hierarchy are important tasks in developmental biology and may help overcome drug resistance in treatment of cancer and other diseases. Single-cell technologies provide a powerful tool for detecting rare cell types and cell-fate transition events, whereas traditional gene expression profiling methods can be used only to measure the average behavior of a cell population. However, the lack of suitable computational methods for single-cell data analysis has become a bottleneck. Here we present a method with the focuses on automatically detecting multilineage transitions and on modeling the associated changes in gene expression patterns. We show that our method is generally applicable and that its applications provide biological insights into developmental processes.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 111 (52), 5643-, 2014-12-15
Proceedings of the National Academy of Sciences