Journal
NATURE METHODS
Volume 14, Issue 10, Pages 967-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.4427
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Funding
- NCI [K08CA175143, P01HL088594, R01 CA133253, R01 CA185404, R01 CA184984]
- Conquer Cancer Foundation Young Investigator Award from the Scopus Foundation
- NSF [PHY-1545915]
- Stand Up To Cancer
- Institute of Computational Health Sciences (ICHS) at UCSF
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Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question researchers face is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here we develop a data-driven approach, illustrated in the context of image data, that estimates the sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples.
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