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Sensei: how many samples to tell a change in cell type abundance?

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 t...

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Autores principales: Liang, Shaoheng, Willis, Jason, Dou, Jinzhuang, Mohanty, Vakul, Huang, Yuefan, Vilar, Eduardo, Chen, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728970/
https://www.ncbi.nlm.nih.gov/pubmed/34983369
http://dx.doi.org/10.1186/s12859-021-04526-5
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author Liang, Shaoheng
Willis, Jason
Dou, Jinzhuang
Mohanty, Vakul
Huang, Yuefan
Vilar, Eduardo
Chen, Ken
author_facet Liang, Shaoheng
Willis, Jason
Dou, Jinzhuang
Mohanty, Vakul
Huang, Yuefan
Vilar, Eduardo
Chen, Ken
author_sort Liang, Shaoheng
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04526-5.
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spelling pubmed-87289702022-01-06 Sensei: how many samples to tell a change in cell type abundance? Liang, Shaoheng Willis, Jason Dou, Jinzhuang Mohanty, Vakul Huang, Yuefan Vilar, Eduardo Chen, Ken BMC Bioinformatics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04526-5. BioMed Central 2022-01-04 /pmc/articles/PMC8728970/ /pubmed/34983369 http://dx.doi.org/10.1186/s12859-021-04526-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liang, Shaoheng
Willis, Jason
Dou, Jinzhuang
Mohanty, Vakul
Huang, Yuefan
Vilar, Eduardo
Chen, Ken
Sensei: how many samples to tell a change in cell type abundance?
title Sensei: how many samples to tell a change in cell type abundance?
title_full Sensei: how many samples to tell a change in cell type abundance?
title_fullStr Sensei: how many samples to tell a change in cell type abundance?
title_full_unstemmed Sensei: how many samples to tell a change in cell type abundance?
title_short Sensei: how many samples to tell a change in cell type abundance?
title_sort sensei: how many samples to tell a change in cell type abundance?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728970/
https://www.ncbi.nlm.nih.gov/pubmed/34983369
http://dx.doi.org/10.1186/s12859-021-04526-5
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