Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data

摘要

Mass cytometry is a new high-throughput technology that is becoming a cornerstone in immunology and cell biology research. With technological advancement, the number of cellular characteristics cytometry can simultaneously quantify grows, making analysis increasingly computationally onerous. In this paper, we investigate the potential of dimensionality reduction techniques to ease computational burden in clustering cytometry data whilst minimally diminishing clustering performance. We explore 3 such techniques: Principal Component Analysis (PCA), Autoencoders (AE) and Uniform Manifold Approximation and Projection (UMAP). Thereafter we employ a recent clustering algorithm, ChronoClust, which clusters data at each time-point into cell populations and explicitly tracks them over time. We evaluate this approach through a 14-dimensional cytometry dataset describing the immune response to West Nile Virus over 8 days in mice. To obtain a broad sample of clustering performance, each of the four datasets (unreduced, PCA-, AE- and UMAP-reduced) is independently clustered 400 times, using 400 unique ChronoClust parameter value sets. We find that PCA and AE can reduce the computational expense whilst incurring a minimal degradation in clustering and cluster tracking performance.