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scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Keegan D. Korthauer, Li-Fang Chu, Michael A. Newton, Yuan Li, James Thomson, Ron Stewart, Christina Kendziorski
doi: https://doi.org/10.1101/035501
Keegan D. Korthauer
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 02215, Boston, MA, USA.
2Department of Biostatistics, Harvard T. H. Chan School of Public Health, 02115, Boston, MA, USA.
Li-Fang Chu
3Morgridge Institute for Research, University of Wisconsin, 53706, Madison, WI, USA.
Michael A. Newton
4Department of Biostatistics, University of Wisconsin, 53706, Madison, WI, USA.
5Department of Statistics, University of Wisconsin, 53706, Madison, WI, USA.
Yuan Li
5Department of Statistics, University of Wisconsin, 53706, Madison, WI, USA.
James Thomson
3Morgridge Institute for Research, University of Wisconsin, 53706, Madison, WI, USA.
6Department of Cell and Regenerative Biology, University of Wisconsin, 53706, Madison, WI, USA.
7Department of Molecular, Cellular, and Developmental Biology, University of California, 93106, Santa Barbara, CA, USA.
Ron Stewart
3Morgridge Institute for Research, University of Wisconsin, 53706, Madison, WI, USA.
Christina Kendziorski
4Department of Biostatistics, University of Wisconsin, 53706, Madison, WI, USA.
5Department of Statistics, University of Wisconsin, 53706, Madison, WI, USA.
Article usage
Posted December 29, 2015.
scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Keegan D. Korthauer, Li-Fang Chu, Michael A. Newton, Yuan Li, James Thomson, Ron Stewart, Christina Kendziorski
bioRxiv 035501; doi: https://doi.org/10.1101/035501
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