ssIHDB

ssIHDB is a comprehensive resource for ischemic heart disease (IHD) research with an well-annotated single-cell atlas and a genetic, pharmacological, and comorbid knowledge graph.

Introduction to ssIHDB

Spatial Single-cell Ischemic Heart Disease Browser (ssIHDB, https://xomics.com.cn/ihdb) is a comprehensive, spatio-temporally resolved resource that integrates single-cell and spatial transcriptomic profiles with a manually curated knowledgebase of genes, drugs, and comorbidities relevant to IHD. ssIHDB is composed of a single-cell atlas of IHD (IHDAtlas) encompassing over 1.4 million cells that can be categorized into 40 basic cell types, and a IHD knowledge graph (IHDKG) with manually curated 6,035 CHD-related genes, 3,157 drugs, 80 comorbidities, and gene-disease associations and molecular interactions between them. Furthermore, ssIHDB incoperates a spatial atlas that integrates 54 mouse myocardial slices from critical time points after infarction and defined 14 spatial clusters with distinct locations and functions. To make ssIHDB a reliable reference for IHD research, we performed collective meta-program (MP) analysis on both single-cell and spatial atlases and generated 16 single-cell MPs and 9 spatial MPs associated with different murine disease models. Each MP is a representitive of a group of gene programs under disease states. A pipeline based on IHDAtlas was used to analyze every single-cell and spatial sample, with all results, including differential gene expression, cell-cell interaction, pathway activeness, spatial mapping, deconvolution, and MPs were visulized on our website. The IHDKG was so far the largest manually curated knowledge graph for ischemic heart disease. It provides the most genes, drugs, and gene-disease associations with supporting literature comparing to other dedicated databases. Besides, IHDKG has a temporal dimension that each gene was annotated with prevalence in cell subtypes under different disease conditions. Our website provides batch search and fast network creation tool for users to build gene-gene, gene-drug, and gene-comorbidity networks.

To run our analysis pipeline, we suggest to install Anaconda on Windows PC and create a new environment using the sc.yml configuration to install all related packages.

conda env create -f sc.yml -n sc
conda activate sc

Besides, an R environment should be configured to include all related packages. The required R packages include:

NMF==0.27  
scry==1.16.0  
scran==1.32.0  
Biobase==2.62.0  
BiocParallel==1.36.0  
DESeq2==1.38.3  
anndata==0.7.5.6  
Matrix==1.6-5  
zinbwave==1.28.0  
caret==7.0-1  
msigdbr==7.5.1  
gsdensity==0.1.3  

Before preprocessing scRNA-seq data, users should reformat and organize their scRNA-seq matrix data into one of the following structures:

1. 10x  
   GSEXXXXXX  
    ├─GSMXXXXXXX  
    │      barcodes.tsv.gz  
    │      features.tsv.gz  
    │      matrix.mtx.gz  
    │  
    ├─GSMXXXXXXX  
    │      barcodes.tsv.gz  
    │      features.tsv.gz  
    │      matrix.mtx.gz  
    ...  
2. 10x tiled  
   GSEXXXXXX  
       GSMXXXXXXX_barcodes.tsv.gz  
       GSMXXXXXXX_features.tsv.gz  
       GSMXXXXXXX_matrix.mtx.gz  
       GSMXXXXXXX_barcodes.tsv.gz  
       GSMXXXXXXX_features.tsv.gz  
       GSMXXXXXXX_matrix.mtx.gz  
       ...  
3. csv  
   GSEXXXXXX  
        GSMXXXXXXX.csv  
        GSMXXXXXXX.csv  
        ...  
5. txt  
   GSEXXXXXX  
        GSMXXXXXXX.txt  
        GSMXXXXXXX.txt  
        ...  
6. h5  
   GSEXXXXXX  
        GSMXXXXXXX.h5  
        GSMXXXXXXX.h5  
        ...  
7. h5ad  
   GSEXXXXXX  
        GSMXXXXXXX.h5ad  
        GSMXXXXXXX.h5ad  
        ...  

Put all dataset folders under the same folder. Run "preprocess_QC.ipynb".

For single-cell analysis of individual samples, run "Annotation.py" first for every preprocessed files, then run "HVG.R" for all files, finally "PATHWAY.R", "NMF.R", and "run_EMBEDandCCI.py".
For meta-program analysis, run "generate_robust_programs.ipynb" first, then run "generate_meta_programs.R", which is modified from the orginal "Generate_Meta_Programs.R".
For spatial analysis, just run each spatial analysis script individually. As we only provide the modified code for meta-program generation, for complete and orginal codes please refer to https://github.com/tiroshlab/3ca/tree/main/ITH_hallmarks

About

SsIHDB has been published in APSB. Please refer to Wang Tianhao et al., An integrated resource for ischemic heart disease defines hallmarks and heterogeneity across time and space, Acta Pharmaceutica Sinica B, https://doi.org/10.1016/j.apsb.2025.11.020.

Should you have any questions, please feel free to contact the author, Mr. Tianhao Wang (woloorn@zju.edu.cn)