spatialproteomics

spatialproteomics enables end-to-end analysis of highly multiplexed immunofluorescence imaging data through a Python-based framework that synchronizes shared coordinates across images, segmentation masks, and expression matrices.

Key Features:

• Out-of-core image processing: Processes images larger than working memory by leveraging xarray and dask.
• Coordinate synchronization: Supports synchronization of shared coordinates across data modalities such as images, segmentation masks, and expression matrices for safe subsetting and transformation.
• End-to-end workflow coverage: Integrates segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis.
• Gigapixel image support: Can process gigapixel whole slide images.

Scientific Applications:

• Cancer tissue characterization: Enables statistical characterization of cell type composition and spatial distribution across indolent and aggressive B cell Non-Hodgkin lymphomas (BNHL).
• Reactive lymph node analysis: Supports analysis of reactive lymph node tissue samples at single-cell resolution on the protein level.
• Paraffin-embedded tissue analysis: Facilitates analysis of paraffin-embedded tissue samples with high scalability and low cost.

Methodology:

Built on top of xarray and dask. Processes segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis.