Identification of spatially variable genes with graph cuts

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.