Learning Bayesian Networks
Feature-guided clustering of multi-dimensional flow cytometry datasets
Journal of Biomedical Informatics
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Statistical file matching of flow cytometry data
Journal of Biomedical Informatics
EM algorithms for multivariate Gaussian mixture models with truncated and censored data
Computational Statistics & Data Analysis
Computer Methods and Programs in Biomedicine
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In the rapidly advancing field of flow cytometry, methodologies facilitating automated clinical decision support are increasingly needed. In the case of B-Chronic Lymphocytic Leukemia (B-CLL), discrimination of the various subpopulations of blood cells is an important task. In this work, our objective is to provide a useful paradigm of computer-based assistance in the domain of flow-cytometric data analysis by proposing a Bayesian methodology for flow cytometry clustering. Using Bayesian clustering, we replicate a series of (unsupervised) data clustering tasks, usually performed manually by the expert. The proposed methodology is able to incorporate the expert's knowledge, as prior information to data-driven statistical learning methods, in a simple and efficient way. We observe almost optimal clustering results, with respect to the expert's gold standard. The model is flexible enough to identify correctly non canonical clustering structures, despite the presence of various abnormalities and heterogeneities in data; it offers an advantage over other types of approaches that apply hierarchical or distance-based concepts.