Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Methods Mol Biol. 2021;2194:177-186. doi: 10.1007/978-1-0716-0849-4_10.
Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.
肿瘤异质性可由多种外在和内在因素引起，并导致不良结局。随着最近技术的进步，单细胞 RNA 测序已成为研究人员在转录组水平上轻松分析肿瘤异质性的一种方法，具有高分辨率。然而，目前的研究重点是分析这种大数据的不同方法，以及如何在多个不同样本之间进行比较。在本章中，我们提供了一个实用指南，用于从单细胞 RNA 测序数据集中计算样本间和样本内多样性度量。这些多样性度量指标是从统计学和生态学中常用的度量指标改编而来的，用于量化和比较单细胞分辨率下的样本异质性。
