Optimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysis

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dc.contributor.authorJeong, Haejinen_US
dc.contributor.authorJeong, Hyoung-ohen_US
dc.contributor.authorLee, Seminen_US
dc.contributor.authorJeong, Won-Kien_US
dc.contributor.editorWimmer, Michaelen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWestermann, RĂĽdigeren_US
dc.date.accessioned2025-11-07T08:33:09Z
dc.date.available2025-11-07T08:33:09Z
dc.date.issued2025
dc.description.abstractSingle-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.en_US
dc.description.number6
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70151
dc.identifier.issn1467-8659
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70151
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70151
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsCC BY-NC-ND Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectscientific visualisation, visual analytics, visualisation, visualisation ;Human-centred computing→Heat maps; Visual analytics
dc.subjectscientific visualisation
dc.subjectvisual analytics
dc.subjectvisualisation
dc.subjectvisualisation
dc.subjectHuman-centred computing→Heat maps
dc.subjectVisual analytics
dc.titleOptimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysisen_US
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