Optimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysis
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Single-cell RNA sequencing (scRNA-seq) has gained prominence as a valuable technique for examining cellular gene expression patterns at the individual cell level. In the analysis of scRNA-seq datasets, it is common practice to visualise a subset of principal components (PCs), obtained via principal component analysis (PCA), using dimensionality reduction techniques such as t-stochastic neighbour embedding (t-SNE). Determining the number of PCs (i.e. dimensionality) is a critical step that influences the outcome of single-cell analysis, and this process typically requires a labour-intensive manual assessment involving the inspection of numerous projection plots. To address this challenge, we present a visualisation system that assists analysts in efficiently determining the optimal dimensionality of scRNA-seq data. The proposed system employs two hull heatmaps, a cell type heatmap and a cluster heatmap, which offer comprehensive representations of target cells of multiple cell types across various dimensionalities through the utilisation of a convex hull-embedded colour map. The cell type heatmap shows overlaps between cell types, and the cluster heatmap compares cell clustering results. The proposed hull heatmaps effectively alleviate the labourious task of manually evaluating hundreds of projection plots for searching for the optimal dimensionality. Additionally, our system offers interactive visualisation of gene expression levels and an intuitive lasso selection tool, thereby enabling analysts to progressively refine the convex hulls on the hull heatmaps. We validated the usefulness of the proposed system through two quantitative evaluations and three case studies.
Description
@article{10.1111:cgf.70151,
journal = {Computer Graphics Forum},
title = {{Optimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysis}},
author = {Jeong, Haejin and Jeong, Hyoung-oh and Lee, Semin and Jeong, Won-Ki},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70151}
}