Authors:Andrew J Steindl, João Felipe Rocha, Brian Tshilengi Di Bassinga, Zachary Warren, Matthew Scicluna, César Miguel Valdez Córdova, Shabarni Gupta, Leire Torices, Daniel Neumann, Timothy J. Mann, Ihuan Gunawan, Dhananjay Bhaskar, John G Lock, Christine L Chaffer, Guy Wolf, Smita Krishnaswamy
Abstract:High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these datasets determine the types of information that can be extracted. For example, clustered data supports cell-type identification, trajectory structures support transition analysis, and archetypal structures capture continua of cellular behaviors. Existing analysis pipelines often assume a specific shape. The standard Seurat pipeline combines UMAP visualization with Louvain clustering and therefore assumes clustered data, while tools such as Monocle and SPADE assume tree-like structures, and flow-based models such as MIOFlow and Conditional Flow Matching target trajectories. Choosing which pipeline to apply is therefore often left to bioinformaticians who visually inspect datasets before selecting an analysis strategy. With the rise of agentic AI scientists, automating shape detection is increasingly important for selecting downstream analysis pipelines. To address this problem, we introduce scShapeBench, a benchmark dataset for shape detection containing both synthetic and expert-annotated single-cell datasets. Synthetic datasets are sampled from ground-truth skeleton graphs with controlled variance. Real single-cell datasets are curated from diverse sources and annotated by experts into four categories: clusters, single trajectory, multi-branching, and archetypal. We additionally introduce scReebTower, a baseline method that uses diffusion geometry to extract Reeb graphs and connect visualization with pipeline selection. We provide topology-aware evaluation metrics and compare scReebTower against PAGA and Mapper on synthetic and real data. Our results indicate that scReebTower outperforms existing baselines. Overall, our contributions span benchmarks, evaluation metrics, and a baseline for automated shape detection in single-cell data.
| Subjects: | Machine Learning (cs.LG); Genomics (q-bio.GN) |
| Cite as: | arXiv:2605.12662 [cs.LG] |
| (or arXiv:2605.12662v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12662 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Andrew Steindl [view email]
[v1]
Tue, 12 May 2026 19:10:38 UTC (1,674 KB)