Cytomulate: accurate and efficient simulation of CyTOF data

et al. 2022

Preprint

Self Cite

Recently developed single-cell profiling technologies, such as single cell sequencing (scRNA-seq) and mass cytometry (CyTOF), provide critical biological insights from a large number of cells and a large number of features. While experimental and informatic techniques around scRNA-seq are much more advanced, research around CyTOF data analysis has severely lagged behind. However, CyTOF profiles the proteomics makeup of the single cells and are more closely related to disease phenotypes than scRNA-seq. CyTOF data are also dramatically different from scRNA-seq data in many aspects. This calls for the evaluation and development of statistical methods for CyTOF data. Dimension reduction (DR) is one of the most critical first steps of analyses of scRNA-seq and CyTOF data. Here, we benchmark 20 of these methods on 110 real and 425 synthetic CyTOF datasets, including 10 Imaging CyTOF datasets, for accuracy, scalability and usability. In particular, we checked the concordance of DR for CyTOF data against scRNA-seq data that were generated from the same samples. We found that SAUCIE and scvis surprisingly perform well across all categories, whereas UMAP particularly excels in downstream analyses and MDS preserves local and global structure well. Our results highlight the complementarity of existing tools, and that the choice of method should depend on the underlying data structure of the single cells. Based on these results, we develop a set of guidelines to help users select the best DR method for their dataset. Our freely available data and evaluation pipeline will aid in the development of improved DR algorithms specifically tailored to analyze data generated from the increasingly popular CyTOF technique.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Comparative Analysis of Dimension Reduction Methods for Cytometry by Time-of-Flight Data

et al. 2022

Preprint

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Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

et al. 2023

Nat Commun

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While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

Cytomulate: accurate and efficient simulation of CyTOF data

et al. 2022

Preprint

Self Cite

Cytometry by time-of-flight (CyTOF) is a proteomic analysis technique capable of providing information on millions of cells. Along with technological development, researchers have devised many data analysis tools to offer deeper insights into the data at the single-cell level. However, objective evaluations of these methods remain absent. In this paper, we develop Cytomulate: an accurate, efficient, and well-documented simulation model and algorithm for reproducible and accurate simulation of CyTOF data, which could serve as a foundation for future development and evaluation of new methods. With its two simulation modes: creation mode and emulation mode, Cytomulate is capable of generating simulated datasets that capture various characteristics of CyTOF data under different circumstances. We also provide the users with methods to simulate batch effects, temporal effects, and cell differentiation paths. Empirical experiments suggest that Cytomulate is superior in learning the overall data distribution than other single-cell RNA-seq-oriented methods such as Splatter.