{"repo":"davisidarta/topometry","free":true,"listed":false,"github":"https://github.com/davisidarta/topometry","clone":"git clone https://github.com/davisidarta/topometry.git","description":"Systematically learn and evaluate the latent geometry of high-dimensional data, with a focus on scRNAseq analysis","language":"Python","stars":107,"topics":["clustering","data-visualization","dimensionality-reduction","graph","graph-layout","hypothesis-generation","laplace-beltrami","machine-learning","manifold-learning","scikit-learn"],"license":"MIT","category":"machine-learning","readme_excerpt":"About TopoMetry TopoMetry is a geometry-aware Python toolkit for exploring high-dimensional data via diffusion/Laplacian operators. It learns neighborhood graphs → Laplace–Beltrami–type operators → spectral scaffolds → refined graphs and then finds clusters and builds low-dimensional layouts for analysis and visualization. - AnnData/Scanpy wrappers for single-cell workflows - scikit-learn–style transformers with a high-level orchestrator - Fixed-time & multiscale spectral scaffolds (no .X mutation; namespaced outputs) - Operator-native metrics to quantify geometry preservation and Riemannian diagnostics to evaluate distortion in visualizations - Designed for large, diverse datasets (e.g., single-cell omics) For background, see our preprint: https://doi.org/10.1101/2022.03.14.484134 Geometry-first rationale (short) We approximate the Laplace–Beltrami operator (LBO) by learning well-weighted similarity graphs and their Laplacian/diffusion operators. The eigenfunctions of these operators form an orthonormal basis—the spectral scaffold —that captures the dataset’s intrinsic geometry across scales. This view connects to Diffusion Maps , Laplacian Eigenmaps , and related kernel eigenmaps, and enables downstream tasks such as clustering and graph-layout optimization with geometry preserved. When to use TopoMetry Use TopoMetry when you want: - Geometry-faithful representations beyond variance maximization (e.g., PCA) - Robust low-dimensional views and clustering from operator-grounde","default_branch":null,"files":null,"tree":[],"storefront":"/r/davisidarta","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/davisidarta/topometry/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}