Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering

作者：Kokturk Basak Esin^*; Karacali Bilge

来源：International Journal of Data Mining and Bioinformatics, 2016, 14(1): 86-99.

DOI：10.1504/IJDMB.2016.073365

• expectation maximisation algorithm
• quasi-supervised learning algorithm
• clustering
• gating of flow cytometry data
• simulated annealing algorithm
• flow cytometry data analysis
• bioinformatics
• data mining

This paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions are continued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters or their distribution models.

• 出版日期2016