Unveiling the power of high-dimensional cytometry data with cyCONDOR
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 develo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55179-w |
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| author | Charlotte Kröger Sophie Müller Jacqueline Leidner Theresa Kröber Stefanie Warnat-Herresthal Jannis Bastian Spintge Timo Zajac Anna Neubauer Aleksej Frolov Caterina Carraro DELCODE Study Group Frank Jessen Simone Puccio Anna C. Aschenbrenner Joachim L. Schultze Tal Pecht Marc D. Beyer Lorenzo Bonaguro |
| author_facet | Charlotte Kröger Sophie Müller Jacqueline Leidner Theresa Kröber Stefanie Warnat-Herresthal Jannis Bastian Spintge Timo Zajac Anna Neubauer Aleksej Frolov Caterina Carraro DELCODE Study Group Frank Jessen Simone Puccio Anna C. Aschenbrenner Joachim L. Schultze Tal Pecht Marc D. Beyer Lorenzo Bonaguro |
| author_sort | Charlotte Kröger |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a6d1b5abe5c41189a33d993e3202a9f |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-1a6d1b5abe5c41189a33d993e3202a9f2024-12-22T12:36:22ZengNature PortfolioNature Communications2041-17232024-12-0115111610.1038/s41467-024-55179-wUnveiling the power of high-dimensional cytometry data with cyCONDORCharlotte Kröger0Sophie Müller1Jacqueline Leidner2Theresa Kröber3Stefanie Warnat-Herresthal4Jannis Bastian Spintge5Timo Zajac6Anna Neubauer7Aleksej Frolov8Caterina Carraro9DELCODE Study GroupFrank Jessen10Simone Puccio11Anna C. Aschenbrenner12Joachim L. Schultze13Tal Pecht14Marc D. Beyer15Lorenzo Bonaguro16Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1Laboratory of Translational Immunology, IRCCS Humanitas Research Hospital, via Manzoni 56Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)Systems Medicine, German Center for Neurodegenerative Diseases (DZNE)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.https://doi.org/10.1038/s41467-024-55179-w |
| spellingShingle | Charlotte Kröger Sophie Müller Jacqueline Leidner Theresa Kröber Stefanie Warnat-Herresthal Jannis Bastian Spintge Timo Zajac Anna Neubauer Aleksej Frolov Caterina Carraro DELCODE Study Group Frank Jessen Simone Puccio Anna C. Aschenbrenner Joachim L. Schultze Tal Pecht Marc D. Beyer Lorenzo Bonaguro Unveiling the power of high-dimensional cytometry data with cyCONDOR Nature Communications |
| title | Unveiling the power of high-dimensional cytometry data with cyCONDOR |
| title_full | Unveiling the power of high-dimensional cytometry data with cyCONDOR |
| title_fullStr | Unveiling the power of high-dimensional cytometry data with cyCONDOR |
| title_full_unstemmed | Unveiling the power of high-dimensional cytometry data with cyCONDOR |
| title_short | Unveiling the power of high-dimensional cytometry data with cyCONDOR |
| title_sort | unveiling the power of high dimensional cytometry data with cycondor |
| url | https://doi.org/10.1038/s41467-024-55179-w |
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