@article{6056c9aa5efe47c8a5d04ff7c22fb1da,
title = "Sampling strategies to capture single-cell heterogeneity",
abstract = "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.",
author = "Satwik Rajaram and Heinrich, {Louise E.} and Gordan, {John D.} and Jayant Avva and Bonness, {Kathy M.} and Witkiewicz, {Agnieszka K.} and Malter, {James S.} and Atreya, {Chloe E.} and Warren, {Robert S.} and Wu, {Lani F.} and Altschuler, {Steven J.}",
note = "Funding Information: We thank M. Calvert, T.D. Tlsty and P.B. Stark for helpful discussions. This work was supported by the NCI K08CA175143 (C.E.A.), P01HL088594 (J.S.M.), a Conquer Cancer Foundation Young Investigator Award from the Scopus Foundation (J.D.G.), a gift from the Edmund Wattis Littlefield Foundation (R.S.W.), NSF PHY-1545915 (S.J.A.), Stand Up To Cancer (S.J.A.), NCI R01 CA133253 (S.J.A.), NCI RO1 CA185404 (L.F.W.) and NCI R01 CA184984 (L.F.W.), and the Institute of Computational Health Sciences (ICHS) at UCSF (S.J.A. and L.F.W.). Publisher Copyright: {\textcopyright} 2017 Nature America, Inc., part of Springer Nature. All rights reserved.",
year = "2017",
month = oct,
day = "1",
doi = "10.1038/nmeth.4427",
language = "English (US)",
volume = "14",
pages = "967--970",
journal = "PLoS Medicine",
issn = "1549-1277",
publisher = "Public Library of Science",
number = "10",
}