bio-spatial-transcriptomics-spatial-visualization

Visualize spatial transcriptomics data by generating tissue plots colored by gene expression, cluster assignments, and annotations. You can apply this to standard Squidpy-Scanpy workflows to explore spatial patterns across single- or multi-sample datasets.

Yes, you can generate tissue plots with an optional histology image background. This overlay provides visual context for spatial gene expression patterns and cluster assignments directly on the tissue sample.

Compare spatial patterns across multiple samples by generating tissue plots for each condition. The visualization workflow integrates with standard Squidpy and Scanpy patterns to help you identify differences in spatial gene expression and clusters.

You need Python with Squidpy and Scanpy installed, along with compatible Matplotlib versions. These packages provide the foundation for generating tissue plots and integrating the visualization code into your analysis pipelines.

Yes, the workflow generates publication-ready figures from your spatial transcriptomics data. You can customize visuals for tissue plots colored by gene expression and clusters, then export the results directly for publication use.

You can integrate the code patterns for generating spatial transcriptomics tissue plots into automation pipelines. The usage patterns described in the workflow guide integration with standard Squidpy and Scanpy analyses for reproducibility.