bio-spatial-transcriptomics-image-analysis

What problem does it solve?

This skill enables researchers to bridge tissue imaging with spatial transcriptomics by automatically extracting image-derived features, segmenting cells or nuclei, and computing morphology metrics to enrich downstream gene-expression analyses.

Core Features & Use Cases

• Image feature extraction per spatial spot and per molecule with Squidpy, enabling integrated multimodal analyses.
• Cell/nuclei segmentation from tissue images using Watershed or Cellpose backends, improving spot-level morphology understanding.
• Morphological feature computation and integration with expression data for clustering, domain discovery, and visualization in spatial studies.
• Use Case: combine image-derived features with expression PCA to refine spatial domains and interpret tissue architecture.

Quick Start

Provide a spatial tissue image and run the analysis to extract features, segment cells, and compute morphology for downstream interpretation.