CROST

CROST aggregates and standardizes spatial transcriptomic datasets and analytical methods to identify and characterize tumor-associated spatially variable genes (SVGs) and spatial gene-expression patterns.

Key Features:

• Repository content: Houses 182 high-quality spatial transcriptomic datasets subdivided into 1,033 sub-datasets.
• Tumor-related SVGs: Catalogs 48,043 tumor-related spatially variable genes (SVGs).
• Dataset diversity: Includes spatial transcriptomic data derived from diverse species, organs, and diseases.
• Standardized processing pipeline: Applies a standardized spatial transcriptome data processing pipeline for consistency across datasets.
• scRNA-seq deconvolution integration: Integrates single-cell RNA sequencing deconvolution with spatial transcriptomics data.
• Correlation analysis: Enables evaluation of spatial correlation of gene expression.
• Colocalization analysis: Enables assessment of gene and cell-type colocalization within tissue sections.
• Intercellular communication analysis: Supports analysis of intercellular communication in spatial context.
• Biological function annotation: Performs biological function annotation analyses on spatially resolved genes.
• Multi-omics integration: Integrates transcriptomic, epigenomic, and genomic data to investigate tumor-associated SVGs.
• Single-sample gene set enrichment analysis: Implements single-sample gene set enrichment analysis for sample-level functional inference.
• SpatialAP: Provides SpatialAP for spatial annotation and analysis of spatial transcriptomics data.

Scientific Applications:

• Identification of tumor-associated SVGs: Characterizes spatially variable genes related to tumors for cancer biology studies.
• Investigation of cancer progression and prognosis: Links tumor-associated SVGs with cancer progression and prognosis using integrated genomic and epigenomic data.
• Tissue architecture and colocalization studies: Examines spatial correlation and colocalization of genes and cell types within tissues.
• Intercellular communication mapping: Maps intercellular communication networks in their spatial context.
• Functional annotation of spatial genes: Assigns biological function to spatially variable genes via enrichment analyses.
• Comparative spatial analyses: Enables comparative analyses across species, organs, and disease states.

Methodology:

Uses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution with spatial transcriptomics, combines transcriptomic, epigenomic and genomic data, and implements single-sample gene set enrichment analysis and SpatialAP.