Sensei: How many samples to tell evolution in single-cell studies?

Abstract

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.

Single-Cell Expression Landscape of SARS-CoV-2 Receptor ACE2 and Host Proteases in Normal and Malignant Lung Tissues from Pulmonary Adenocarcinoma Patients

“…Interestingly, TMPRSS2 and TMPRSS4 had been previously ascribed diverse roles in mucosal pathobiology, including that of the lung, such as the activation of influenza and SARS-CoV viruses [ 39 , 40 , 41 ], promotion of lung fibrosis [ 42 ], as well as the induction of lung cancer stem-cell-like properties [ 43 ], growth, metastasis, and resistance to chemotherapy [ 44 ]. It is worthwhile to mention that patients with cancer, including lung malignancy, were shown to represent a population that is extremely vulnerable to COVID-19 [ 3 , 45 ]. It is plausible to surmise that a better understanding of the expression of SARS-CoV-2 receptors is highly warranted in lung cancer patients, particularly in LUADs, whose pathogenesis is linked to ACE2 -expressing airway lineages (e.g., AT2), and also since LUADs arise in the lung periphery, a major site for the development of pneumonia in COVID-19 patients.…”

Making the most of high‐dimensional cytometry data

“…In practice, these can often be difficult to determine but can be estimated either from prior experience with the biological system, in pilot experiments, 16 or by examining published studies. An integrated model of the effect of group size, population variance and cell number on statistical power for single‐cell experiments has recently been described, and a web interface is available 17 …”

“…An integrated model of the effect of group size, population variance and cell number on statistical power for single‐cell experiments has recently been described, and a web interface is available. 17 …”