PySpacell

PySpacell performs spatial analysis of single-cell phenotypic and molecular measurements from microscopy, sequential hybridization, and mass spectrometry to detect and quantify spatial effects and estimate their spatial scale.

Key Features:

• Spatial analysis: Performs spatial analysis of single-cell phenotypic and molecular measurements.
• Supported technologies: Processes data originating from microscopy, sequential hybridization, and mass spectrometry.
• Spatial-effect detection: Implements statistical tests to detect spatial effects within microscopy images of adherent cells.
• Spatial scale estimation: Estimates the spatial scale at which spatial effects manifest.
• Data types: Handles light microscopy images and single-cell in situ transcriptomics and metabolomics data.
• Input formats: Accepts standard output formats from CellProfiler, Fiji, and Icy.
• Quantitative in situ assessment: Enables quantitative in situ assessments at the cellular level.
• Statistical approach: Applies statistical approaches for spatial data analysis.

Scientific Applications:

• Spatial heterogeneity analysis: Maps and analyzes spatial heterogeneity of phenotypes and molecular measurements at single-cell resolution.
• Cell-cell interaction studies: Detects and quantifies how spatial arrangements influence cell-cell interactions and microenvironmental effects.
• Adherent cell imaging: Tests for spatial effects specifically in microscopy images of adherent cells.
• In situ omics analysis: Applies to in situ transcriptomics and metabolomics datasets to assess spatial organization of molecular signals.

Methodology:

Performs statistical testing for spatial effects and estimation of spatial scale on single-cell measurements derived from microscopy, sequential hybridization, or mass spectrometry.