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Department seminar by Lynn Lin from The Pennsylvania State UniversityExport this event to calendar

Thursday, September 22, 2016 — 4:00 PM EDT

Clustering with Hidden Markov Model on Variable Blocks

Advances in single-cell cytometry technologies have enabled high-dimensional measurement of individual cells in a high-throughput manner. A key first step to analyze this wealth of data is to identify distinct cell subsets from a mixed-population sample such as blood or tissue. In many clinical applications, cell subsets of interest are often found in very low frequencies. This poses challenges for existing clustering methods. To address this issue, we propose a new mixture model, namely the Hidden Markov Model on Variable Blocks (HMM-VB). HMM-VB incorporates prior knowledge on the chain-like dependence structure among groups of variables, achieving the effect of dimension reduction as well as incisive modeling of the rare clusters. In a series of experiments on simulated and real data, HMM-VB outperforms other widely used methods.

Location 
M3 - Mathematics 3
3127

,
Canada
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