bio-spatial-transcriptomics-spatial-deconvolution

To estimate cell-type proportions in spatial transcriptomics spots, you load your spatial data and scRNA-seq reference, intersect genes, train a model, and run deconvolution using cell2location, Tangram, SPOTlight, or RCTD.

Visium spatial transcriptomics deconvolution is best handled by loading a matched scRNA-seq reference to infer per-spot cell-type proportions. Methods like cell2location and RCTD support Visium-like datasets across various tissues.

Yes, you can run cross-method comparisons across tissues. The workflow supports integrated multi-method deconvolution using cell2location, Tangram, SPOTlight, and RCTD to identify dominant cell-type neighborhoods.

Yes, these spatial deconvolution workflows require a Python-based environment to handle reference loading, gene intersection, model training, deconvolution, and visualization for your spatial transcriptomics data.

Spatial transcriptomics deconvolution requires gene intersection to align the spatial spots with the scRNA-seq reference. This ensures the model trains only on overlapping genes to accurately infer cell-type proportions.

Use spatial deconvolution to map tumor microenvironment composition when you need to identify dominant cell-type neighborhoods in spatial tissue sections, leveraging scRNA-seq reference data to estimate per-spot cell-type proportions.