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.
Footnotes
Contact: Peng Wang, 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, 320 Yueyang Road, Shanghai, PR China, 200031 Telephone: 86-21-54920532, Fax: 86-21-54920533 E-mail: wangpeng{at}picb.ac.cn
The second paragraph of the introduction is updated to clarify the scalabilities of spatialDE and trendSceek. In the discussion, the text is updated to clarify that optimization of hyperparameters may limit the performance of Gaussian process and marked point process.