<i>CyAnno</i>: A semi-automated approach for cell type annotation of mass cytometry datasets

Kaushik, Abhinav; Dunham, Diane; He, Ziyuan; Manohar, Monali; Desai, Manisha; Nadeau, Kari; Andorf, Sandra

doi:10.1101/2020.08.28.272559

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…To motivate the design choices of Cytomulate, we have explored prominent characteristics of CyTOF using various public datasets and in-house datasets from UTSWMC (e.g., Finck (5), Levine_13dim (27), Levine_32dim (27), CytoNorm (7), CyAnno (28), Covid (14)). In this section, we mainly used the protein expressions associated with the first patient in the dataset Levine_13dim which is publicly available via the R package HDCytoData (29) for the purpose of demonstration.…”

Section: Resultsmentioning

confidence: 99%

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Cytomulate: accurate and efficient simulation of CyTOF data

Yang

Wang

et al. 2022

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

“…To demonstrate the versatility of Cytomulate, six publicly available datasets from different species and diverse anatomic sites are collected: Levine_32dim (27), Levine_13dim (27), Samusik (39), CyAnno (28), Covid (14), and LG (40). If multiple samples exist for one dataset, the first sample is used.…”

Section: Resultsmentioning

confidence: 99%

Cytomulate: accurate and efficient simulation of CyTOF data

Yang

Wang

et al. 2022

Preprint

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“…Table 2 ). These include the TuPro cohort(9), the Oetjen cohort(22), the CyAnno cohort(33), the LG cohort(34), the Brain cohort(35), the BC cohort(36), the Levine32 cohort(37), the Levine13 cohort(37), the Samusik cohort(38), the Lung cancer cohort(39), and the Covid cohort. For all datasets, we either obtained their cell typing results from the original publications, or performed cell typing ourselves based on clustering of the cells according to their proteomics features followed by manual assignment according to expression of marker genes.…”

Section: Methodsmentioning

confidence: 99%

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

Wang

Yang

et al. 2022

Preprint

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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.

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“…This is in part due to the number of gates required to fully parse the dataset. As such, a variety of computational approaches have been adopted by the cytometry community to help analyze HD datasets, including automated gating [6], clustering (such as PhenoGraph [7], FlowSOM [8], X‐Shift [9]), dimensionality reduction (such as t‐SNE [10, 11], UMAP [12]), trajectory inference (such as Wanderlust [1], Wishbone [13]), and automated cell classification [14–16]. Many of these tools have been brought together into ‘toolboxes’, providing either code‐ or GUI‐based analysis workflows, such as Cytofkit [17], CITRUS [18], CATALYST [19], Cytofworkflow [20], or diffcyt [21].…”

Section: Introductionmentioning

confidence: 99%

“…FlowSOM [8], X-Shift [9]), dimensionality reduction (such as t-SNE [10,11], UMAP [12]), trajectory inference (such as Wanderlust [1], Wishbone [13]), and automated cell classification [14][15][16]. Many of these tools have been brought together into 'toolboxes', providing either code-or GUI-based analysis workflows, such as Cytofkit [17], CITRUS [18], CATALYST [19], Cytofworkflow [20], or diffcyt [21].…”

Section: Introduction 1| High-dimensional Analysis Toolsmentioning

confidence: 99%

Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre

et al. 2021

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As the size and complexity of high‐dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end‐to‐end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre‐processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single‐cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

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CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

Cited by 4 publications

References 31 publications

Cytomulate: accurate and efficient simulation of CyTOF data

Cytomulate: accurate and efficient simulation of CyTOF data

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

Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre

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