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 any 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.

Unsupervised discovery of tissue architecture in multiplexed imaging

“…UTAG performed significantly better than the baselines of random domain permutations and cell type identities, as well as SpaGene 25 and SpatialLDA 26 (Figs. 2 and 4b).…”

“…As a baseline comparison, we calculated the same metrics based on randomly shuffled domain labels and cell type identities. In addition, we also compare UTAG to other methods for inference of higher-level tissue structure in terms of their features and performance, such as SpaGene 25 and SpatialLDA 26 (Fig. 2b and Extended Data Fig.…”

“…To benchmark UTAG against other methods for high-order tissue structure inference, we ran SpaGene 25 and SpatialLDA 26 on both datasets for which we have ground truth annotation of microanatomical domains. For this purpose, we also reran UTAG using a max_dist of 15 for both datasets, under Leiden clustering resolutions of 0.05, 0.07, 0.1, 0.3, 0.5, 0.8, 1.0 and 2.0, which resulted in 3,5,10,11,14,17,19,25,31 and 55 clusters for the healthy lung data, and 3, 4, 6, 22, 23, 27, 38 and 61 clusters for the UTUC data. We intentionally do not use the interpreted annotations in Fig.…”

An information theoretic approach to detecting spatially varying genes

“…Giotto [Dries et al, 2021], in addition to interfacing with SpatialDE, trendsceek, and SPARK, implements its own SVG test called binary spatial extraction (BinSpect) which binarizes expression values using either K-means clustering (BinSpect-kmeans) or threshold ranking (BinSpect-rank) and runs a Fisher's exact test using a contingency table of values from neighboring cells. SpaGene [Liu et al, 2022] also heuristically binarizes expression data, but instead considers the degree distribution for a subgraph of the k nearest neighbor graph consisting of just high expression cells, operating under the principle that if high expression cells colocate we would expect an increase in higher degree nodes in this graph when compared to a shuffled graph.…”