Cases on 10x Visium Data

Here we provide case applications based on 10x Visium data (which are not at single-cell resolution). For convenience, we used the Quick Mode here, but you can also follow the Step by Step Guide to analyze 10x Visium data—the steps are the same.

A frequently asked question is how to provide annotations for 10x Visium data. Note that gsMap can run without annotations. The most convenient approaches are to either leave the annotation parameter unset (in Step by Step) or provide annotations from spatial clustering methods, such as SpaGCN.

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

You can download the example 10x Visium data as follows:

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

Directory structure:

tree -L 2

Visium_example_data/
├── GWAS
│   ├── IQ_NG_2018.sumstats.gz
│   └── Serum_creatinine.sumstats.gz
└── ST
    ├── V1_Adult_Mouse_Brain_Coronal_Section.h5ad
    ├── V1_Mouse_Brain_Sagittal_Posterior_Section.h5ad
    └── V1_Mouse_Kidney.h5ad

Case1

Data: Visium data of adult mouse coronal section Trait: IQ Required memory: 11G (2902 cells)

gsmap quick_mode \
    --workdir './example_quick_mode/Visium' \
    --homolog_file 'gsMap_resource/homologs/mouse_human_homologs.txt' \
    --sample_name 'V1_Adult_Mouse_Brain_Coronal_Section' \
    --gsMap_resource_dir 'gsMap_resource' \
    --hdf5_path 'Visium_example_data/ST/V1_Adult_Mouse_Brain_Coronal_Section.h5ad' \
    --annotation 'domain' \
    --data_layer 'count' \
    --sumstats_file 'Visium_example_data/GWAS/IQ_NG_2018.sumstats.gz' \
    --trait_name 'IQ'

gsMap report for the IQ on the adult mouse coronal section Visium data.

Case2

Data: Visium data of adult mouse sigital section Trait: IQ

Required memory: 12G (3289 cells)

gsmap quick_mode \
    --workdir './example_quick_mode/Visium' \
    --homolog_file 'gsMap_resource/homologs/mouse_human_homologs.txt' \
    --sample_name 'V1_Mouse_Brain_Sagittal_Posterior_Section' \
    --gsMap_resource_dir 'gsMap_resource' \
    --hdf5_path 'Visium_example_data/ST/V1_Mouse_Brain_Sagittal_Posterior_Section.h5ad' \
    --annotation 'domain' \
    --data_layer 'count' \
    --sumstats_file 'Visium_example_data/GWAS/IQ_NG_2018.sumstats.gz' \
    --trait_name 'IQ'

gsMap report for the IQ on the adult mouse sigital section Visium data.

Case3

Data: Visium data of adult mouse kindey Trait: Serum creatinine

Required memory: 8G (1437 cells)

gsmap quick_mode \
    --workdir './example_quick_mode/Visium' \
    --homolog_file 'gsMap_resource/homologs/mouse_human_homologs.txt' \
    --sample_name 'V1_Mouse_Kidney' \
    --gsMap_resource_dir 'gsMap_resource' \
    --hdf5_path 'Visium_example_data/ST/V1_Mouse_Kidney.h5ad' \
    --annotation 'domain' \
    --data_layer 'count' \
    --sumstats_file 'Visium_example_data/GWAS/Serum_creatinine.sumstats.gz' \
    --trait_name 'Serum_creatinine'

gsMap report for the Serum creatinine on the adult mouse kindey Visium data.