Stereopy

Stereopy models and dissects comparative and spatiotemporal patterns in multi-sample spatial transcriptomics data.

Key Features:

• Universal container: A data structure for multi-sample multimodal data storage and management.
• Scope controller: A component for managing data processing across multi-sample spatial transcriptomics datasets.
• Integrative transformer: A method tailored for processing multi-sample multimodal spatial transcriptomics data.
• Interactive data visualization: Built-in visualization capabilities for spatial transcriptomics data interpretation.
• Cell community investigation: Identification of specific cell communities and genes responsible for pathological changes.
• Spatiotemporal gene pattern detection: Detection of gene patterns by integrating spatial and temporal features.
• Three-dimensional niche-based interaction network inference: Inference of cell-gene interaction networks that bridge intercellular communications and intracellular regulations in 3D spatial niches.

Scientific Applications:

• Pathological change analysis: Investigation of specific cell communities and genes responsible for pathological changes in multi-sample spatial transcriptomics data.
• Spatiotemporal gene pattern analysis: Detection of gene patterns that incorporate both spatial and temporal features across samples.
• 3D niche-based cell-gene interaction inference: Reconstruction of three-dimensional cell-gene interaction networks linking intercellular communications with intracellular regulations.

Methodology:

Stereopy employs a universal container for data storage, a scope controller for data management, and an integrative transformer for processing multi-sample multimodal spatial transcriptomics data.