Scalable and model-free detection of spatial patterns and colocalization

Abstract

The expeditious growth in spatial omics technologies enable profiling genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover spatial patterns rapidly in large scale spatial omics studies. Analyzing simulation and a variety of spatial resolved transcriptomics data demonstrated that SpaGene is more powerful and scalable than existing methods. Spatial expression patterns by SpaGene reconstructed unobserved tissue structures. SpaGene also successfully discovered ligand-receptor interactions through their colocalization.

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

“…Next, we performed two types of enrichment analysis. First, we compared the performance of the methods in different layers by computing their enrichment scores (ES) following Liu et al (2022) [42]. It is based on the expectation that the genes which abundantly express themselves in the four spatial layers, should be identified and ranked top by the methods.…”

“…We did not focus on the common genes because they have been extensively studied in earlier literature, such as the work of Liu. et al ( 2022) [42]. The genes Npy1r and Cplx3 belonged to set (a), and are known to be enriched in inhibitory and excitatory neurons [60,61].…”

“…In the real data analysis, we computed the enrichment scores (ES) of the three methods following the procedure outlined in Liu et al (2022) [42]. Cell clustering based on biological knowledge or using popular softwares, such as RCTD [32] and Seurat [33], with the transcriptional profiles, can often identify spatially localized layers or cell types.…”

“…Some of the notable methods of type (a) include Trendsceek [36], SpatialDE [34], SPARK [37], and SPARK-X [38]. Some of the methods of type (b) are HMRF [39], MERINGUE [40], SpaGCN [41] and SpaGene [42]. Being model-based, type (a) methods allow adjusting for covariates such as cell type, whereas type (b) methods claim to be more flexible in the sense of being model-free.…”

“…On the other side, a popular method of type (b), MERINGUE [40] considers spatial autocorrelation and cross-correlation based on spatial neighborhood graphs to identify SVGs. Improving hugely on the complexity of MERINGUE, another model-free method named SpaGene [42] has been recently developed. It constructs a spatial network between cells/spots using the k -nearest neighbors approach, and then for each gene, extracts the subnetwork whose nodes have high gene expression.…”