TY  - JOUR
AU  - Kröger, Charlotte
AU  - Müller, Sophie
AU  - Leidner, Jacqueline
AU  - Kroeber, Theresa
AU  - Warnat-Herresthal, Stefanie
AU  - Spintge, Jannis Bastian
AU  - Zajac, Timo
AU  - Neubauer, Anna
AU  - Frolov, Aleksej
AU  - Carraro, Caterina
AU  - Jessen, Frank
AU  - Puccio, Simone
AU  - Aschenbrenner, Anna C
AU  - Schultze, Joachim L
AU  - Pecht, Tal
AU  - Beyer, Marc D
AU  - Bonaguro, Lorenzo
TI  - Unveiling the power of high-dimensional cytometry data with cyCONDOR.
JO  - Nature Communications
VL  - 15
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DZNE-2025-00036
SP  - 10702
PY  - 2024
AB  - 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.
KW  - Software
KW  - Flow Cytometry: methods
KW  - Computational Biology: methods
KW  - Humans
KW  - Single-Cell Analysis: methods
KW  - Algorithms
KW  - Animals
KW  - Machine Learning
KW  - Mice
KW  - Cluster Analysis
LB  - PUB:(DE-HGF)16
C6  - pmid:39702306
C2  - pmc:PMC11659560
DO  - DOI:10.1038/s41467-024-55179-w
UR  - https://pub.dzne.de/record/274055
ER  -