Mouse Embryo (Quick Mode)

The Quick Mode option provides a simplified and efficient way to execute the entire gsMap pipeline. It minimizes running time and configuration complexity by utilizing pre-calculated weights based on the 1000G EUR reference panel and protein-coding genes from gtf file of Gencode v46. This mode is ideal for users who prefer a streamlined approach. For a more customizable experience, such as using custom GTF files, reference panels, and adjustable parameters, please refer to the Step by Step guide.

Preparation

Make sure you have installed the gsMap package before proceeding.

1. Download Dependencies

The gsMap package in quick mode requires the following resources:

  • Gene transfer format (GTF) file, for gene coordinates on the genome.

  • LD reference panel, in quick mode, we provide a pre-built LD score snp-by-gene matrix based on 1000G_EUR_Phase3.

  • SNP weight file, to adjust correlations between SNP-trait association statistics.

  • Homologous gene transformations file (optional), to map genes between species.

To download all the required files:

wget https://yanglab.westlake.edu.cn/data/gsMap/gsMap_resource.tar.gz
tar -xvzf gsMap_resource.tar.gz

Directory structure:

tree -L 2

gsMap_resource
    ├── genome_annotation
    │   ├── enhancer
    │   └── gtf
    ├── homologs
    │   ├── macaque_human_homologs.txt
    │   └── mouse_human_homologs.txt
    ├── LD_Reference_Panel
    │   └── 1000G_EUR_Phase3_plink
    ├── LDSC_resource
    │   ├── hapmap3_snps
    │   └── weights_hm3_no_hla
    └── quick_mode
        ├── baseline
        ├── SNP_gene_pair
        └── snp_gene_weight_matrix.h5ad

2. Download Example Data

To run the quick mode example, you can download the example data as follows:

wget https://yanglab.westlake.edu.cn/data/gsMap/gsMap_example_data.tar.gz
tar -xvzf gsMap_example_data.tar.gz

Directory structure:

tree -L 2

gsMap_example_data/
├── GWAS
│   ├── BCX2_MCHC_EA_GWAMA.sumstats.gz
│   ├── GIANT_EUR_Height_2022_Nature.sumstats.gz
│   ├── gwas_config.yaml
│   └── IQ_NG_2018.sumstats.gz
└── ST
    └── E16.5_E1S1.MOSTA.h5ad

Running gsMap in Quick Mode

Required memory: 80G (120K cells) 

gsmap quick_mode \
    --workdir './example_quick_mode/Mouse_Embryo' \
    --homolog_file 'gsMap_resource/homologs/mouse_human_homologs.txt' \
    --sample_name 'E16.5_E1S1.MOSTA' \
    --gsMap_resource_dir 'gsMap_resource' \
    --hdf5_path 'gsMap_example_data/ST/E16.5_E1S1.MOSTA.h5ad' \
    --annotation 'annotation' \
    --data_layer 'count' \
    --sumstats_file 'gsMap_example_data/GWAS/IQ_NG_2018.sumstats.gz' \
    --trait_name 'IQ'

Parameters

  • --workdir: The working directory where output files will be saved.

  • --homolog_file: The homologous gene file for converting gene names from different species to human.

  • --sample_name: The name of the sample (e.g., E16.5_E1S1.MOSTA).

  • --gsMap_resource_dir: Path to the directory containing the gsMap resources.

  • --hdf5_path: Path to the input HDF5 file with spatial transcriptomics (ST) data.

  • --annotation: The name of the annotation column in the adata.obs of the input HDF5 file.

  • --data_layer: The layer of the gene expression matrix (e.g., count).

  • --sumstats_file: Path to the GWAS summary statistics file.

  • --trait_name: Name of the trait (e.g., IQ).

Additional Options

  • If you want to analyze multiple traits at once, provide a configuration file (--sumstats_config_file) instead of a single summary statistics file:

gsmap quick_mode \
    --workdir './example_quick_mode/Mouse_Embryo' \
    --homolog_file 'gsMap_resource/homologs/mouse_human_homologs.txt' \
    --sample_name 'E16.5_E1S1.MOSTA' \
    --gsMap_resource_dir 'gsMap_resource' \
    --hdf5_path 'gsMap_example_data/ST/E16.5_E1S1.MOSTA.h5ad' \
    --annotation 'annotation' \
    --data_layer 'count' \
    --sumstats_config_file 'gsMap_example_data/GWAS/gwas_config.yaml'

The gwas_config.yaml file includes the following:

Height: gsMap_example_data/GWAS/GIANT_EUR_Height_2022_Nature.sumstats.gz
IQ: gsMap_example_data/GWAS/IQ_NG_2018.sumstats.gz
SCZ: gsMap_example_data/GWAS/PGC3_SCZ_wave3_public_INFO80.sumstats.gz

Output Description

  • The output files will be saved in the specified --workdir directory and will include intermediate files such as latent representations, gene marker scores, and LD scores. These intermediate files will be reused if you analyze another GWAS trait of the same sample within this --workdir.

  • A web report will be generated in the report folder, which includes visualizations of spatial cell-trait associations and diagnostic plots. To view the report, copy this folder to your local machine and open the HTML file in a web browser. You can refer to this example report for the IQ trait.

Example Output Structure

After running the analysis in quick mode, the following directory structure will be created:

tree -L 3

example_quick_mode/Mouse_Embryo
├── E16.5_E1S1.MOSTA
│   ├── find_latent_representations  # Contains latent representations in h5ad format
│   ├── latent_to_gene               # Gene marker scores
│   ├── generate_ldscore             # LD scores
│   ├── spatial_ldsc                 # Spatial cell-trait association results
│   ├── cauchy_combination           # Region-level or cell type-level association results
│   └── report                       # Web report with visualizations and diagnostics