Identification of spatially variable genes with graph cuts
Identification of spatially variable genes with graph cuts
Abstract Single-cell gene expression data with positional information are critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by current data analysis strategies. Here, we present scGCO (single-cell graph cuts optimization), a method based on fast optimization of Markov Random Fields with graph cuts, to identify spatially viable genes. Extensive benchmarking demonstrated that scGCO delivers superior performance with optimal segmentation of spatial patterns, and can process millions of cells in a timely manner owing to its linear scalability.
Wang Peng、Feng Wanwan、Zhang Ke
Bio-med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences||School of Life Science and Technology, ShanghaiTech UniversityBio-med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of SciencesBio-med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences
生物科学研究方法、生物科学研究技术生物化学生物物理学
graph cuts optimizationspatial gene expressionsingle-cell
Wang Peng,Feng Wanwan,Zhang Ke.Identification of spatially variable genes with graph cuts[EB/OL].(2025-03-28)[2025-05-17].https://www.biorxiv.org/content/10.1101/491472.点此复制
评论