Overview of clinical flow cytometry data analysis: recent advances and future challenges

Area of Science:

• Biomedical research
• Clinical diagnostics
• Data science

Background:

• Recent decades have seen major technological advancements in flow cytometry (FC) instrumentation and reagents.
• These advances enable simultaneous analysis of more parameters per cell at increased speeds.
• This leads to the generation of large, complex datasets with information on millions of cells and tens of parameters.

Purpose of the Study:

• To review current data analysis and visualization strategies for flow cytometry (FC) data.
• To highlight methods that improve the objectivity and robustness of FC data interpretation.
• To address the challenges posed by increasingly complex FC datasets.

Main Methods:

• Review of conventional and newly developed data analysis algorithms.
• Examination of visualization techniques for high-dimensional flow cytometry data.
• Synthesis of strategies for interpreting complex FC datasets.

Main Results:

• Identification of a growing need for advanced data analysis tools in FC.
• Overview of emerging algorithms and software for handling large-scale FC data.
• Discussion of strategies for objective and robust interpretation of FC results.

Conclusions:

• New data analysis and visualization tools are essential for managing complex flow cytometry data.
• These tools facilitate more objective and robust interpretation of FC data.
• The reviewed strategies are applicable to both biomedical research and clinical diagnostic settings.