Map single-cell reference annotations onto spatial transcriptomics data using Tangram deep learning alignment. Projects cell type labels from scRNA-seq reference to spatial spots/cells. Works for both cell-level and spot-level (deconvolution-like) data.
Analyze cell-cell communication using CellPhoneDB statistical method to identify significant ligand-receptor interactions between cell types via permutation testing.
Analyze cell-cell communication using LIANA+ to identify significant ligand-receptor interactions between cell types. (Recommended!)
Annotate cell types using unsupervised clustering, marker genes, and LLM-based annotation. Fast alternative to reference-based methods.
Annotate cell types in spatial transcriptomics data using scANVI transfer learning from a reference scRNA-seq dataset. (Recommended for cell data)
Perform cell type deconvolution (or annotation on spot) on spatial transcriptomics data (Visium spots) using RCTD with a single-cell reference dataset. (Recommended for spot data)
Reference-based Bayesian deconvolution of spot-level spatial transcriptomics using Cell2location. Two-stage model that first learns cell type expression signatures from scRNA-seq reference, then maps them to spatial spots. Provides uncertainty estimates. GPU recommended.
Ultra-fast reference-based cell type deconvolution for spot-level spatial data using FlashDeconv. O(N) complexity via random sketching — much faster than RCTD or Cell2location. Pure Python, no GPU needed.