Introducing cyCONDOR: A User-Friendly Tool for High-Dimensional Cytometry Data Analysis
Author Information
Author(s): Charlotte Kröger, Sophie Müller, Jacqueline Leidner, Theresa Kröber, Stefanie Warnat-Herresthal, Jannis Bastian Spintge, Timo Zajac, Anna Neubauer, Aleksej Frolov, Caterina Carraro, Freiesleben Silka Dawn, Slawek Altenstein, Boris Rauchmann, Ingo Kilimann, Marie Coenjaerts, Annika Spottke, Oliver Peters, Josef Priller, Robert Perneczky, Stefan Teipel, Emrah Düzel, Frank Jessen, Simone Puccio, Anna C. Aschenbrenner, Joachim L. Schultze, Tal Pecht, Marc D. Beyer, Lorenzo Bonaguro
Primary Institution: German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Hypothesis
An integrated, simple to use, end-to-end ecosystem for HDC data analysis would overcome current shortcomings and enable HDC users to take full advantage of the high dimensionality of the data.
Conclusion
cyCONDOR is a comprehensive and easy-to-use computational framework that enhances the analysis of high-dimensional cytometry data.
Supporting Evidence
- cyCONDOR provides a versatile workflow for data pre-processing.
- cyCONDOR offers a suite of microservices for data import and pre-processing.
- cyCONDOR implements Harmony for batch alignment over multiple sources of technical variation.
- cyCONDOR allows the end user to fully exploit the potential of HDC methods.
Takeaway
cyCONDOR is a new tool that helps scientists analyze complex data from cells easily, making it simpler to understand how different cells behave.
Methodology
cyCONDOR provides a comprehensive data analysis toolkit encompassing data ingestion and transformation, batch correction, dimensionality reduction, and clustering, along with streamlined functions for data visualization, biological comparison, and statistical testing.
Potential Biases
The reliance on pre-selected markers limits the use of reference mapping techniques.
Limitations
Cell type identification is still a laborious process and cannot be automated yet.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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