ArticleBMC bioinformatics2022
Sensei: how many samples to tell a change in cell type abundance?
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Multimodal profiling of atherosclerosis: Protocol and pilot data for the AtherOMICS biobank.Science advances · 2026Article
- VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.Nature protocols · 2026Review
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Immune impacts of fire smoke exposure.Nature medicine · 2025Article
- Spatial iTME analysis of KRAS mutant NSCLC and immunotherapy outcome.NPJ precision oncology · 2024Article
- Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.Biomolecules · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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 .
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Registered trials
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