# SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets

## TL;DR

SEPAR is a new computational tool that helps analyze spatial transcriptomics data by identifying localized gene patterns and improving the understanding of spatial molecular interactions.

## Contribution

SEPAR introduces a novel unsupervised framework using spatial metagenes to uncover localized gene programs and spatial molecular interactions in SRT and multi-omics data.

## Key findings

- SEPAR identifies biologically meaningful gene ontologies and cell types linked to metagene patterns.
- SEPAR detects spatially variable genes with higher accuracy and enhances biological signals through gene refinement.
- SEPAR uncovers co-localized molecule correlations in spatial CITE-seq and gene-peak relationships in MISAR-seq.

## Abstract

Spatially resolved transcriptomics (SRT) profiles gene expressions at near- or sub-cellular resolution while preserving their spatial context, yet interpreting SRT data to understand spatial cellular and molecular organization remains challenging. Most existing computational methods focus on global spatial domains but overlook localized structures driven by specific gene subsets. Here, we introduce SEPAR, an unsupervised framework that leverages spatial metagenes to analyze SRT data by integrating gene activity and spatial neighborhood relationships. It enables multiple downstream analyses including: identifying metagene pattern-specific genes, detecting spatially variable genes (SVGs), delineating spatial domains, and refining expression signals. Evaluated on diverse datasets, SEPAR reveals biologically meaningful gene ontologies and cell types in gene sets linked to metagene patterns, identifies SVGs with higher accuracy, and enhances biological signals with gene refinement. In spatial multi-omics data, it uncovers co-localized molecule correlations in spatial CITE-seq and coordinated gene-peak relationships in MISAR-seq, offering insights into spatial molecular interactions.

SEPAR is a computational approach that uses spatial metagenes to reveal localized gene programs, identify spatially variable genes, delineate spatial domains, and refine gene expressions for spatial transcriptomics, and uncover coordinated interactions for spatial multi-omics.