Tangram is for mapping single-cell (or single-nucleus) gene expression data onto spatial gene expression data. The single-cell dataset and the spatial dataset should be collected from the same anatomical region/tissue type, ideally from a biological replicate, and need to share a set of genes. Tangram aligns the single-cell data in space by fitting gene expression on the shared genes. Spatial data need to be organized as a voxel-by-gene matrix. The voxel coordinates are saved in the fields obs.x and obs.y which we can use to visualize the spatial ROI. Each "dot" is the center of a 10um voxel.

Example use case: The most common application of Tangram is to resolve cell types in space. Another usage is to correct gene expression from spatial data: as scRNA-seq data are less prone to dropout than (e.g.) Visium or Slide-seq, the "new" spatial data generated by Tangram resolve many more genes. As a result, we can visualize program usage in space, which can be used for ligand-receptor pair discovery or, more generally, cell-cell communication mechanisms. If cell segmentation is available, Tangram can be also used for deconvolution of spatial data. If your single cell are multimodal, Tangram can be used to spatially resolve other modalities, such as chromatin accessibility.