Flow cytometry (FCM) determines the characteristics of individual biological cells using optical and fluorescence measurements. It is a widely used standard method for analysing blood samples in medical diagnostics, through identifying and quantifying the different types of cells in the samples. The multidimensional dataset obtained from FCM is large and complex, so it is difficult and time-consuming to analyse manually. The main process of differentiation, and therefore, labelling of the different cell populations in the data is referred to as gating. This is the first step of FCM and is highly subjective, an issue that significant research has focussed on reducing. Existing automated gating techniques are time-consuming or retain subjectivity by requiring many user-defined parameters. This paper presents FLOPTICS: a novel automated gating technique that is a combination of density-based and grid-based clustering algorithms. FLOPTICS has an ability to classify and label cell populations in FCM data faster and with fewer user-defined parameters than many state-of-the-art techniques.
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