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

•Polychromatic flow cytometry generates increasingly complex n-dimensional data sets. •New tools are being created for objective flow cytometry data analysis/interpretation. •Full automation of data analysis remains a challenge. Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories. Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories.