Cellenium

Cellenium provides interactive visual analytics and integration for multimodal single-cell sequencing data, enabling exploration of RNA-seq, ATAC-seq, and CITE-seq datasets.

Key Features:

• Scalability: Handles large volumes of multimodal single-cell sequencing data for analysis.
• Integration Across Modalities: Integrates RNA-seq, ATAC-seq, and CITE-seq data for unified analyses.
• Cross-Study Analysis: Enables comparison and joint analysis across multiple single-cell studies.
• Interactive Cell Annotation: Allows defining and labeling cell types based on analysis results.
• Data Visualization: Generates plots using plotlyjs, seaborn, vega-lite, and nivo.rocks.
• Backend Implementation: Uses PostgreSQL, Python 3, and GraphQL for data management and querying.
• Deployment: Distributed as a dockerized application via Docker Compose.

Scientific Applications:

• Cellular Heterogeneity Analysis: Supports detailed characterization of cellular heterogeneity from single-cell datasets.
• Multimodal Mechanistic Studies: Enables joint analysis of gene expression, chromatin accessibility, and protein levels to investigate molecular mechanisms.
• Cell Type Annotation and Marker Identification: Facilitates defining cell types and identifying marker features across modalities.
• Cross-Study Comparison and Meta-analysis: Supports comparing findings across studies to identify conserved or differential signatures.

Methodology:

Server backend implemented with PostgreSQL, Python 3, and GraphQL; plots generated with plotlyjs, seaborn, vega-lite, and nivo.rocks; application packaged via Docker Compose.