Flow Cytometry Data Analysis

Flow Cytometry device is frequently used in the analysis of blood samples. Analysis of Flow Cytometry data is needed in cases such as diagnosing disease, monitoring progression of disease. However, the manual analysis of these multi-dimensional data by the hand cannot be performed at the desired level due to various reasons. In this study, it is aimed to create an algorithm which allows the gating process by manual gating to be performed automatically by the experts in the flow cytometry data. The algorithm consists of k-means, Gaussian Mixture Method (GMM) clustering methods, Expectation Maximization (EM) algorithm and Chernoff distance measurement method. The algorithm developed in the scope of the study was tested on the DLBCL dataset and a success rate of 86.06% was obtained.

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