Hidden Markov Models on Variable Blocks with a Modal Clustering Algorithm and Applications

TL;DR

This paper introduces a hierarchical mixture model called HMM-VB and a mode search algorithm MBW for efficient clustering of large-scale cytometry data, especially for identifying rare cell subsets.

Contribution

The paper presents a novel HMM-VB model and MBW algorithm tailored for high-dimensional, large-scale cytometry data with rare clusters, improving clustering efficiency and accuracy.

Findings

HMM-VB outperforms existing methods on simulated data.

MBW efficiently overcomes exponential complexity.

Effective identification of rare cell subsets in cytometry data.

Abstract

Motivated by high-throughput single-cell cytometry data with applications to vaccine development and immunological research, we consider statistical clustering in large-scale data that contain multiple rare clusters. We propose a new hierarchical mixture model, namely Hidden Markov Model on Variable Blocks (HMM-VB), and a new mode search algorithm called Modal Baum-Welch (MBW) for efficient clustering. Exploiting the widely accepted chain-like dependence among groups of variables in the cytometry data, we propose to treat the hierarchy of variable groups as a figurative time line and employ a HMM-type model, namely HMM-VB. We also propose to use mode-based clustering, aka modal clustering, and overcome the exponential computational complexity by MBW. In a series of experiments on simulated data HMM-VB and MBW have better performance than existing methods. We also apply our method to identify rare cell subsets in cytometry data and examine its strengths and limitations.