spatial-tutorials

Spatial transcriptomics workflows involve sequential image preprocessing, segmentation, deconvolution, and cell communication analysis. This Skill provides step-by-step instructions and code snippets to process spatial data, normalize values, and execute downstream analysis across various biological tissues and imaging modalities.

Cell deconvolution in spatial transcriptomics resolves mixed cellular signals from tissue spots into distinct cell type proportions. You need it when analyzing platforms where spot resolution covers multiple cells, enabling accurate tumor microenvironment characterization and downstream cell communication modeling.

Yes, this spatial analysis workflow supports Visium data preprocessing, cell type deconvolution, and spatial communication analysis specifically within tumor microenvironments. It accommodates various biological tissues and imaging modalities for multi-modal data integration.

The best way to integrate multi-modal spatial data involves structured workflow steps including normalization, tissue integration, and trajectory inference. This approach handles complex integration across different imaging modalities and biological tissues for comprehensive spatial transcriptomics analysis.

Yes, you need familiarity with bioinformatics and spatial analysis techniques to run these workflows. The Skill performs advanced deconvolution, segmentation, and multi-modal integration requiring prior knowledge of spatial transcriptomics methods and data processing concepts.