scBlood

scBlood catalogs single-cell accessible chromatin sequencing (scATAC-seq) data from blood cells across various tissues and disease types to facilitate investigation of blood cell functions, regulatory mechanisms, and involvement in blood-related diseases.

Key Features:

• Data Collection: scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from approximately 400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types.
• Data Preprocessing: All data undergo preprocessing from raw fastq files and multiple standards of quality control.
• Multi-sample Integration Analysis: The database conducts multi-sample integration analysis across collected datasets.
• Cell Clustering and Annotation: scBlood performs cell clustering and annotation on the scATAC-seq data.
• Differential Chromatin Accessibility Analysis: The platform includes differential chromatin accessibility analysis.
• Functional Enrichment Analysis: scBlood performs functional enrichment analysis.
• Co-accessibility Analysis: The database includes co-accessibility analysis.
• Gene Activity Score Calculation: scBlood calculates gene activity scores.
• Transcription Factor Enrichment Analysis: The platform performs transcription factor (TF) enrichment analysis.

Scientific Applications:

• Blood Cell Regulatory Mechanism Research: scBlood facilitates insights into the functions and regulatory mechanisms of blood cells at the single-cell level.
• Blood-related Disease Studies: The platform enables investigation of blood cell involvement in blood-related diseases through chromatin accessibility analysis.
• Comparative Tissue Analysis: scBlood supports cross-tissue comparison of blood cell chromatin accessibility patterns across 30 tissues.
• Transcription Factor Regulatory Network Analysis: The database enables transcription factor enrichment analysis to explore regulatory networks in blood cells.

Methodology:

All data hosted on scBlood are preprocessed from raw fastq files with multiple standards of quality control. Downstream analyses include multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor enrichment analysis.