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11 16, 2023;24 (1): 262.

Cytomulate: accurate and efficient simulation of CyTOF data.

Yuqiu Yang, Kaiwen Wang, Zeyu Lu, Tao Wang, Xinlei Wang
Author Information
  1. Yuqiu Yang: Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA.
  2. Kaiwen Wang: Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA.
  3. Zeyu Lu: Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA.
  4. Tao Wang: Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. tao.wang@utsouthwestern.edu.
  5. Xinlei Wang: Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA. xinlei.wang@uta.edu. ORCID
PMID: 37974276 DOI: 10.1186/s13059-023-03099-1

Abstract

Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

Keywords

CyTOF Proteomics Simulation

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Grants

  1. R01 CA258584/NCI NIH HHS
  2. R15 GM131390/NIGMS NIH HHS
  3. U01 AI156189/NIAID NIH HHS

MeSH Term

Computer Simulation
Algorithms
Single-Cell Analysis
Flow Cytometry
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't
Article has an altmetric score of 5

Word Cloud

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