annotation-tangram

To map scRNA-seq cell types onto spatial transcriptomics data, this workflow uses Tangram to project cell-type labels from an annotated single-cell reference onto spatial coordinates. You provide a reference .h5ad file, a spatial dataset, and configure the cell-type key.

Yes, you can perform spot-level deconvolution using a single-cell reference. The Tangram workflow supports both cell-level annotation and spot-level deconvolution, projecting scRNA-seq cell-type labels to spatial locations for accurate tissue characterization.

To run Tangram spatial mapping, you need an annotated scRNA-seq reference dataset in .h5ad format and a matching spatial transcriptomics dataset. You also need to configure the cell-type key and mapping parameters to execute the pipeline.

Yes, this workflow supports both cell-level annotation and spot-level deconvolution. Tangram projects cell-type labels from scRNA-seq references to spatial locations, enabling accurate spatial characterization of tissues across slices for either resolution.

The best way to annotate a tumor biopsy spatial map is by projecting known cell types from an scRNA-seq reference onto the spatial data using Tangram. This transfers single-cell annotations to reveal the spatial organization of cell populations.

You need marker genes to project scRNA-seq annotations to spatial coordinates because the Tangram workflow selects cell-type markers to align the single-cell reference with the spatial transcriptomics slice, ensuring accurate mapping of labels and spot-level deconvolution.