A Hybrid Mixture Approach for Clustering and Characterizing Cancer Data

TL;DR

This paper introduces a hybrid matrix-free method for efficient clustering and characterization of high-dimensional cancer data, improving convergence speed and accuracy over existing techniques.

Contribution

It presents a novel hybrid computational scheme combining Gaussian mixtures with generalized factor analyzers, enabling scalable analysis of large biomedical datasets.

Findings

Faster convergence than existing methods

High accuracy in breast cancer subtype identification

Effective characterization of lymphoma subtypes

Abstract

Model-based clustering is widely used for identifying and distinguishing types of diseases. However, modern biomedical data coming with high dimensions make it challenging to perform the model estimation in traditional cluster analysis. The incorporation of factor analyzer into the mixture model provides a way to characterize the large set of data features, but the current estimation method is computationally impractical for massive data due to the intrinsic slow convergence of the embedded algorithms, and the incapability to vary the size of the factor analyzers, preventing the implementation of a generalized mixture of factor analyzers and further characterization of the data clusters. We propose a hybrid matrix-free computational scheme to efficiently estimate the clusters and model parameters based on a Gaussian mixture along with generalized factor analyzers to summarize the large number of variables using a small set of underlying factors. Our approach outperforms the existing method with faster convergence while maintaining high clustering accuracy. Our algorithms are applied to accurately identify and distinguish types of breast cancer based on large tumor samples, and to provide a generalized characterization for subtypes of lymphoma using massive gene records.