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. .. image:: _static/schematic.svg :width: 600 :alt: 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. .. toctree:: :maxdepth: 2 :caption: Contents: install tutorials data api release