SPEED

SPEED integrates single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets across multiple species, tissues, and biological contexts to enable comparative and functional single-cell analyses.

Key Features:

• Data Modules: Four data modules organize datasets by scope: Pan (single-cell sequencing datasets from 127 species), Evo (18 evolution-related datasets), Devo (28 development-related studies), and Diz (85 disease-related single-cell atlases).
• C2C (Cell-to-Cell) Module: Explores intercellular communication to characterize cell-cell interactions from single-cell data.
• G2G (Gene-to-Gene) Module: Analyzes genetic regulatory networks to study gene expression regulation.
• S2S (Species-to-Species) Module: Investigates cross-species molecular evolution using comparative single-cell data.
• sMarker Module: Identifies common marker genes for various cell types.
• sSearch Module: Retrieves specific dataset and metadata information.

Scientific Applications:

• Cellular heterogeneity analysis: Investigating cellular heterogeneity across species, tissues, and biological conditions using single-cell data.
• Development and disease mechanism discovery: Understanding molecular mechanisms underlying development and disease pathogenesis via analysis of development and disease atlases.
• Evolutionary and comparative analyses: Exploring evolutionary relationships through comparative single-cell analyses across species.
• Regulatory network and communication pathway analysis: Facilitating study of genetic regulatory networks and intercellular communication pathways from single-cell datasets.

Methodology:

Datasets are integrated and organized into the Pan, Evo, Devo, and Diz modules to enable multi-level and comparative single-cell analyses.