SCInter

SCInter integrates and annotates single-cell RNA sequencing (scRNA-seq) datasets from human and mouse to support analysis of cell heterogeneity and developmental trajectories.

Key Features:

• Extensive Dataset Integration: 115 integrated datasets derived from 1016 samples covering nearly 150 tissues and cell lines, providing sample-level gene expression profiles.
• Comprehensive Cell Marker Repository: Over 8 million cell markers cataloged across 457 distinct cell types.
• Advanced Analytical Capabilities: Includes quality control (QC), clustering, multi-method automatic annotation of cell types, and prediction of cell differentiation trajectories.
• Support for Immunology and Oncology Research: Provides detailed insights into cell heterogeneity relevant to immunological responses and oncogenic pathways.

Scientific Applications:

• Cell heterogeneity and subpopulation analysis: Identification and characterization of cellular subpopulations within heterogeneous scRNA-seq samples.
• Developmental trajectory inference: Prediction and analysis of cell differentiation trajectories from single-cell transcriptomes.
• Cell type annotation and marker discovery: Automated cell-type annotation using multiple methods combined with a large marker repository to support marker identification.
• Immunology and oncology studies: Investigation of immune responses and oncogenic mechanisms using integrated single-cell datasets.

Methodology:

Integration of multiple scRNA-seq datasets with manual curation, application of clustering and annotation algorithms, and embedding of quality control (QC) measures.