Welcome to gsMap’s documentation!

Introduction

gsMap (genetically informed spatial mapping of cells for complex traits) integrates spatial transcriptomics (ST) data with genome-wide association study (GWAS) summary statistics to map cells to human complex traits, including diseases, in a spatially resolved manner.

How to Cite

If you use gsMap in your studies, please cite:

Song, L., Chen, W., Hou, J., Guo, M. & Yang, J. Spatially resolved mapping of cells associated with human complex traits. Nature (2025).

Key Features

  • Spatially-aware High-Resolution Trait Mapping: Maps trait-associated cells at single-cell resolution, offering insights into their spatial distributions.

  • Spatial Region Identification: Aggregates trait-cell association p-values into trait-tissue region association p-values, prioritizing tissue regions relevant to traits of interest.

  • Putative Causal Genes Identification: Prioritizes putative causal genes by associating gene expression levels with cell-trait relevance.

Overview of gsMap Method

gsMap operates on a four-step process:

  1. Gene Specificity Assessment in Spatial Contexts: To address technical noise and capture spatial correlations of gene expression profiles in ST data, gsMap leverages GNNs to identify homogeneous spots for each spot and estimates gene specificity scores by aggregating information from those homogeneous spots.

  2. Linking Gene Specificity to SNPs: gsMap assigns gene specificity scores to single nucleotide polymorphisms (SNPs) based on their proximity to gene transcription start sites (TSS) and SNP-to-gene epigenetic linking maps.

  3. Spatial S-LDSC: To estimate the relevance of spots to traits, gsMap associates stratified LD scores of individual spots with GWAS summary statistics using the S-LDSC framework.

  4. Spatial Region Identification: To evaluate the association of a specific spatial region with traits, gsMap employs the Cauchy combination test to aggregate p-values from individual spots within that spatial region.

Model architecture

Schmatics of gsMap method. For more details about the gsMap, please check out our publication.

Installation

gsMap is available on gsMap GitHub.

How to install gsMap, check out the installation guide

Tutorials

How to use gsMap, check out the tutorials

Online Analysis Service (coming soon)

Users could upload their own GWAS summary statistics data to perform the analysis.

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