PT  - JOURNAL ARTICLE
AU  - Ke Zhang
AU  - Wanwan Feng
AU  - Peng Wang
TI  - Identification of spatially variable genes with graph cuts
AID  - 10.1101/491472
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 491472
4099  - http://biorxiv.org/content/early/2018/12/09/491472.1.short
4100  - http://biorxiv.org/content/early/2018/12/09/491472.1.full
AB  - 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.