Challenges and Adaptations of Model-Based Clustering for Flow and Mass Cytometry

Abstract

Model-based clustering is a statistical approach to cluster analysis, which has been successfully deployed in a number of domains due to its principled framework, clear assumptions, and adaptability. For these reasons, there has been substantial interest in applying model-based clustering methods to flow cytometry and mass cytometry data. The identification of relevant cell populations is a crucial step in the analysis of cytometry data for immunological research. Technological advances have led to a rapid increase in the dimensionality and complexity of cytometry data, prompting significant interest in the use of clustering algorithms in place of traditional manual data analysis techniques for cell population identification. This article highlights how model-based clustering methods, such as mixture models, have been adapted to meet the many interesting and unusual challenges that present themselves to the researcher when analyzing flow and mass cytometry data. These innovations demonstrate that there is considerable potential for further methodological development and collaboration between the cytometry and model- based clustering research communities.