SOMDE: a scalable method for identifying spatially variable genes with self-organizing map

Motivation: Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data. Results: We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in similar to 5 min in large datasets of more than 20 000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use.

Automated summary

SOMDE is an efficient method for identifying genes with spatially variable expression patterns in large-scale spatial expression data. It is faster and provides comparable results compared to existing methods, while being able to produce results quickly in large datasets.