Preface

Technological advancements in cytometry have significantly broadened the spectrum of available markers, enabling the identification and characterization of a diverse array of cellular populations and states. These advancements have facilitated the analysis of variable sample sizes while ensuring standardized procedures.

Cytometry can now measure an extensive panel of markers on each cell in experiments that may involve dozens to hundreds of samples, each containing approximately a million cells. To manage these large data volumes, established methods such as dimension reduction and clustering have been developed. Dimension reduction techniques create a two-dimensional map that optimally synthesizes the information from multiple markers while clustering efficiently organizes cells into distinct groups. These methods are pivotal for the precise analysis and quantification of cell populations, including the abundance and median intensity of each marker.

The insights gleaned from these analyses are vital for comparing different experimental or clinical conditions and for identifying clusters that exhibit significant variations. However, the current identification methods are often limited, relying on statistical software that is not conducive to automation. This limitation complicates the generation of a list of candidate clusters, particularly when establishing a score that optimally combines multiple pieces of information.

In light of these challenges, post-clustering analysis with analycyte becomes a powerful tool for cytometrists, enabling them to work autonomously and efficiently. By providing standardized reports tailored to specific comparisons, cytometrists can independently manage their analyses, fostering a deeper understanding of their data.

As we move towards more complex, high-dimensional data sets, the ability to consistently interpret and compare results becomes increasingly critical. Standardization paves the way for the development of new computational tools and automated analysis methods, which can handle the complexity of modern cytometry data while maintaining the quality and reliability of the results.

Development Background

The analycyte web application was developed by the CiBi platform at the Centre de Recherche en Cancérologie de Marseille (CRCM).

We extend our sincere thanks to the Institut Français de Bioinformatique (IFB) for providing the deployment solutions that have been instrumental in the successful launch and operation of our work.

Conventions Used in This Book

To make comments or ask technical questions about this book, please send a mail to the CRCM CiBi Platform.

Acknowledgments

Thanks to the CRCM cytometry platform, to the cytometrists who tested the application and for their feedback.

Thanks to the projects  library  Krieger, Perzynski, and Dalton (2021) . Github

Tranks to the dreamRs’ datamods R shiny module package Perrier et al. (2024) . Github

Contact

Eugénie Lohmann IE CiBi Platform

Samuel Granjeaud IR PhD. CiBi Platform

Krieger, Nik, Adam Perzynski, and Jarrod Dalton. 2021. Projects: A Project Infrastructure for Researchers. https://CRAN.R-project.org/package=projects.

Perrier, Victor, Fanny Meyer, Samra Goumri, and Zauad Shahreer Abeer. 2024. Datamods: Modules to Import and Manipulate Data in ’Shiny’. https://CRAN.R-project.org/package=datamods.