Metadata-Version: 2.4 Name: SpatialZoomer Version: 1.1.0 Summary: Multi-scale feature analysis of spatial transcriptomics Author-email: Xinqi Li , Yuhan Fan License: MIT License Copyright (c) 2025 Xinqi Li Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Classifier: Programming Language :: Python :: 3 Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Science/Research Classifier: Topic :: Scientific/Engineering :: Bio-Informatics Requires-Python: >=3.8 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: numpy Requires-Dist: matplotlib Requires-Dist: scanpy Requires-Dist: squidpy Requires-Dist: seaborn Requires-Dist: scipy Requires-Dist: kneed Requires-Dist: pandas Requires-Dist: scikit-learn Requires-Dist: pygsp Requires-Dist: python-igraph Requires-Dist: leidenalg Requires-Dist: ipykernel Dynamic: license-file # SpatialZoomer: multi-scale feature analysis of spatial transcriptomics The Python implementation of **SpatialZoomer**, a toolkit for **multi-scale spatial feature analysis** of single-cell resolved spatial transcriptomics via spectral graph signal processing. It scales to millions of cells with high computational efficiency and low hardware requirements. Explore SpatialZoomer's multi-scale results by sliding the scale axis across four datasets on our interactive demo site: 👉 [**Demo Website**](https://li-xinqi.github.io/SpatialZoomer/) 👉 [**Github Website**](https://github.com/Li-Xinqi/SpatialZoomer.git) ## Introduction SpatialZoomer is a novel computational method that enables the multi-scale feature analysis of single-cell resolved spatial transcriptomics like a zoom-capable microscope. By modeling gene expression as signals on spatial neighbor graph and applying spectral graph signal processing, the method efficiently extracts spatial features across multiple scales and reveals biologically meaningful structures. It can automatically identify “critical” scales by partitioning the cross-scale similarity map via dynamic programming. ### Functions - **Zoom-capable analysis across spatial scales** SpatialZoomer enables the detection of spatial structures from **cells**, **niches**, to **domains** across multiple scales. It can cpature biologically meaningful signals at specific scales, such as those related to disease progression in perturbation or longitudinal scenarios. - **Identification of spatially dependent subtypes** As the spatial scale increases, SpatialZoomer progressively integrates spatial context from larger neighborhoods into each cell’s transcriptomic profile, potentially enabling cells from a single cluster at scale 0 to separate into spatially dependent subclusters at larger scales. These spatial contexts can amplify transcriptomic heterogeneity between biologically distinct subtypes. - **Uncovering complex tissue architecture** The extracted multi-scale features can be used to dissect the complex spatial organization. ### Advantages - High computational efficiency with relatively low memory requirements (No GPU required). - Fast runtime, even for large-scale datasets. - Scalable to >1 million cells on desktops. ## System Requirements SpatialZoomer requires only a standard computer with sufficient RAM for in-memory operations and runs without the need for a GPU. It is lightweight and can be executed on **laptops, desktops, or servers**. ## Cite Xinqi Li#, Yuhan Fan#, Yue Han, Wenbo Guo, Jingmin Huang, Nan Yan, Zeyu Chen, Yanhong Wu, Yuxin Miao, Lin Hou, Xuegong Zhang, Zeyu Chen, Jin Gu*. SpatialZoomer: multi-scale feature analysis of spatial transcriptomics. bioRxiv (2025).