Unveiling the power of high-dimensional cytometry data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

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Publication Details

Journal
Nature Communications
Published
2024-12-19
DOI
https://doi.org/10.1038/s41467-024-55179-w
Citations
9
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.10

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Unveiling the power of high-dimensional cytometry data with cyCONDOR

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Nature Communications
Single-cell and spatial transcriptomics
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Unveiling the power of high-dimensional cytometry data with cyCONDOR

Emrah Düzel, Stefanie Warnat‐Herresthal, Silka Dawn Freiesleben, Robert Perneczky, Annika Spottke, Marc Beyer, Oliver Peters, Sophie Müller, Boris‐Stephan Rauchmann, Tal Pecht, Anna C. Aschenbrenner, Jacqueline Leidner, Josef Priller, Caterina Carraro, Anna Neubauer, Joachim L. Schultze, Ingo Kilimann, Lorenzo Bonaguro, Slawek Altenstein, Simone Puccio, Marie Coenjaerts, Charlotte Kröger, Jannis Bastian Spintge, Timo Zajac, A. N. FROLOV, Stefan Teipel, Frank Jessen, Theresa Kröber
article en
9 citations

Abstract

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

Nature CommunicationsVol. 15(1)
University of Bonn (DE), The University of Melbourne (AU), University of Cologne (DE), German Center for Neurodegenerative Diseases (DE), LMU Klinikum (DE), Humboldt-Universität zu Berlin (DE), Peter Doherty Institute (AU), Munich Cluster for Systems Neurology (DE), Institute of Genetic and Biomedical Research (IT), UK Dementia Research Institute (GB), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), National Research Council (IT), IRCCS Humanitas Research Hospital (IT), University of Rostock (DE), Imperial College London (GB), Technical University of Munich (DE), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE), Ludwig-Maximilians-Universität München (DE), Epigenomics (Germany) (DE), University of Sheffield (GB), Otto-von-Guericke-Universität Magdeburg (DE), University of Edinburgh (GB)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Else Kröner-Fresenius-Stiftung, Horizon 2020 Framework Programme
Openalex Percentile: Top 12%
Single-cell and spatial transcriptomics
2.10
DOIOpen Access PDFOpenAlex
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