Dimensionality Explorer for Single-Cell Analysis

Single-cell RNA sequencing (scRNA-seq) is becoming popular in studying the gene expression of cells at the single-cell level. ScRNA-seq enables analysts to characterize cell types, thereby providing a better understanding of dynamic biological processes. In scRNA-seq data analysis, principal component analysis (PCA) is commonly used to reduce at least thousands of dimensions in the raw data to a manageable size so that analysts can visualize and cluster cells to identify different cell types. The conventional process to determine the optimal dimensionality includes a laborious manual review of hundreds of different projection plots. To address this problem, we introduce a dimensionality explorer for single-cell analysis, which is a visualization system that helps analysts to effectively determine the optimal dimensionality of scRNA-seq data. It employs a hull heatmap, which provides a holistic view of overlaps among multiple cell types across various dimensionalities using a convex hull-embedded color map. The hull heatmap effectively reduces the burden of manually reviewing hundreds of projection plots to determine the optimal dimensionality. Our system also provides interactive gene expression level visualization and intuitive lasso selection, thereby allowing analysts to progressively refine the convex hulls of the hull heatmap. We demonstrate the usefulness of the proposed system through a user study and three case studies conducted by domain experts.