Event

Dr. Raphael Gottardo, IRCM, Montreal "Automated Gating of Flow Cytometry Data via Robust Model-Based Clustering"

Wednesday, December 10, 2008 14:30to16:00
McIntyre Medical Building 3655 promenade Sir William Osler, Montreal, QC, H3G 1Y6, CA

Abstract: The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential.

We propose a flexible statistical model-based clustering approach for identifying cell populations in flow cytometry data based on t mixture models with a Box-Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for non- elliptical clusters. We describe an Expectation- Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection.

Using two publicly available datasets, we demonstrate that our proposed methodology provides enough flexibility and robustness to mimic manual gating results performed by an expert researcher. The proposed clustering methodology is well-adapted to automated analysis of flow cytometry data. It tends to give more reproducible results, and helps reduce the significant subjectivity and human time cost encountered in manual gating analysis.

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