AI RESEARCH
scShapeBench: Discovering geometry from high dimensional scRNAseq data
arXiv CS.LG
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ArXi:2605.12662v1 Announce Type: new 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 s cell-type identification, trajectory structures 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.