SCope

SCope provides visualization and analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets to explore transcriptional states and regulatory networks across complex biological systems.

Key Features:

• .loom file format (loompy, Linnarsson Lab): Supports the .loom file format maintained by the Linnarsson Lab via the loompy Python package for handling very large omics datasets.
• High-dimensional scRNA-seq visualization: Visualizes large-scale and high-dimensional single-cell RNA sequencing datasets for exploration of transcriptional states.
• Integration with Drosophila adult brain atlas: Integrates with a single-cell transcriptome atlas of the adult Drosophila melanogaster brain spanning the organism's lifespan.
• Clustering and subclustering: Handles clustering of single cells (87 initial clusters reported) and further subclustering for high-granularity analysis.
• Gene network analysis (SCENIC): Performs gene network analyses using the SCENIC method to reveal regulatory heterogeneity linked to energy consumption.
• Aging-related transcriptional analysis: Enables analysis of aging-associated changes such as an exponential decline in RNA content without loss of neuronal identity.
• Cross-species comparison: Facilitates comparative analyses of cellular diversity and transcriptional states across Drosophila and mammalian models.

Scientific Applications:

• Exploration of transcriptional states: Investigation of transcriptional states across complex biological systems using scRNA-seq data.
• Cellular diversity in Drosophila brain: Analysis of cellular diversity and regulatory states in the adult Drosophila melanogaster brain across the lifespan.
• Regulatory heterogeneity discovery: Identification of regulatory heterogeneity and links to energy consumption via SCENIC-based gene network analysis.
• Aging studies: Tracking aging-associated exponential declines in RNA content while assessing maintenance of neuronal identity.
• Comparative single-cell studies: Comparative analysis of single-cell datasets across species, including Drosophila and mammalian models.

Methodology:

Clustering single cells into 87 initial clusters with subsequent subclustering, and gene network analyses using the SCENIC method.