Rajaram Satwik, Heinrich Louise E, Gordan John D, Avva Jayant, Bonness Kathy M, Witkiewicz Agnieszka K, Malter James S, Atreya Chloe E, Warren Robert S, Wu Lani F, Altschuler Steven J
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
Nat Methods. 2017 Oct;14(10):967-970. doi: 10.1038/nmeth.4427. Epub 2017 Sep 4.
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.
单细胞技术的进步凸显了细胞异质性的普遍性和生物学意义。研究人员面临的一个关键问题是如何设计实验，以忠实地从细胞群体样本中捕捉到异质性的真实范围。在此，我们开发了一种数据驱动的方法（在图像数据的背景下进行说明），该方法可从现有的样本集合中估计对单细胞异质性进行前瞻性研究所需的采样深度。
