Bayesian clustering of flow cytometry data for the diagnosis of B-chronic lymphocytic leukemia.

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.

A perspective for biomedical data integration: design of databases for flow cytometry.

BACKGROUND: The integration of biomedical information is essential for tackling medical problems. We describe a data model in the domain of flow cytometry (FC) allowing for massive management, analysis and integration with other laboratory and clinical information. The paper is concerned with the proper translation of the Flow Cytometry Standard (FCS) into a relational database schema, in a way that facilitates end users at either doing research on FC or studying specific cases of patients undergone FC analysis RESULTS: The proposed database schema provides integration of data originating from diverse acquisition settings, organized in a way that allows syntactically simple queries that provide results significantly faster than the conventional implementations of the FCS standard. The proposed schema can potentially achieve up to 8 orders of magnitude reduction in query complexity and up to 2 orders of magnitude reduction in response time for data originating from flow cytometers that record 256 colours. This is mainly achieved by managing to maintain an almost constant number of data-mining procedures regardless of the size and complexity of the stored information. CONCLUSION: It is evident that using single-file data storage standards for the design of databases without any structural transformations significantly limits the flexibility of databases. Analysis of the requirements of a specific domain for integration and massive data processing can provide the necessary schema modifications that will unlock the additional functionality of a relational database.

Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data.

Prognosis of B-Chronic Lymphocytic Leukemia (B-CLL) remains a challenging problem in medical research and practice. While the parameters obtained by flow cytometry analysis form the basis of the diagnosis of the disease, the question whether these parameters offer additional prognostic information still remains open. In this work, we attempt to provide computer-assisted support to the clinical experts of the field, by deploying a classification system for B-CLL multiparametric prognosis that combines various heterogeneous (clinical, laboratory and flow cytometry) parameters associated with the disease. For this purpose, we employ the naïve-Bayes classifier and propose an algorithm that improves its performance. The algorithm discretizes the continuous classification attributes (candidate prognostic parameters) and selects the most useful subset of them to optimize the classification accuracy. Thus, in addition to the high classification accuracy achieved, the proposed approach also suggests the most informative parameters for the prognosis. The experimental results demonstrate that the inclusion of flow cytometry parameters in our system improves prognosis.