Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Computer Science, Rice University, Houston, TX, USA.
BMC Bioinformatics. 2022 Jan 4;23(1):2. doi: 10.1186/s12859-021-04526-5.
Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .
细胞异质性是癌症演进和转移的基础。单细胞 RNA 测序和质谱流式细胞术等单细胞技术的进步,使我们能够在临床试验和临床前研究中获得的异质癌症样本中检测特定细胞类型的表达谱和丰度。然而,在确定确定细胞类型丰度变化所需的样本量方面仍然存在挑战。为了解决这一统计学挑战,我们开发了一种新的方法,名为 Sensei,用于确定单细胞研究中两组样本之间确定此类变化所需的样本数量和细胞数量。Sensei 扩展了 t 检验,并使用贝塔二项式分布对细胞丰度进行建模。我们评估了 Sensei 的数学准确性,并根据从癌症细胞图谱 (TCGA) 和之前的单细胞研究中获得的知识,针对 30 多种癌症类型中的 20 多种细胞类型提供了实用的指导原则。我们提供了一个网络应用程序,可通过 https://kchen-lab.github.io/sensei/table_beta.html 实现用户友好的研究设计。