Dimension Reduction and Clustering of Single Cell Calcium Spiking: Comparison of t-SNE and UMAP

Abstract

Time-lapse fluorescent imaging of cytosolic calcium is used to detect cellular activity during preclinical experiments and drug screening studies. However, visualization and analysis of high dimension time series data remain challenging due to the presence of underlying heterogeneity. In this context, we propose t-distribution stochastic neighborhood embedding (t-SNE) and uniform manifold projection and approximation (UMAP) for visualization and analysis. Next, we show the density-based spatial clustering of applications with noise (DBSCAN) can be used to detect various spiking patterns present in calcium dose-response. The proposed framework combining t-SNE and DBSCAN was used to find repeating patterns, detect outliers, and label similar instances present in biological signaling.