bio-spatial-transcriptomics-image-analysis

Integrate image-derived features with gene expression data by computing morphology metrics per spatial spot using Squidpy. This combines tissue image characteristics with expression PCA to refine spatial domains and interpret tissue architecture in downstream analyses.

Perform cell and nuclei segmentation on tissue images using Watershed or optional Cellpose backends integrated with Squidpy. This capability improves spot-level morphology understanding for Visium-like spatial transcriptomics workflows across various tissue types.

Tissue image morphology analysis requires proper spatial tissue image data and spatial annotations compatible with Visium-like workflows. You must provide high-resolution tissue images alongside spatial coordinate mappings to successfully extract image features and segment cells.

Spatial image analysis supports Visium-like spatial transcriptomics workflows across various tissue types. It processes tissue images to extract features and segment cells, enabling integration of image morphology with gene expression for spatial domain discovery.

Integrate image morphology with gene expression data to refine spatial domains and interpret tissue architecture. Combining image-derived features with expression PCA enhances clustering resolution and reveals spatial patterns that expression data alone may miss.