scientific-spatial-transcriptomics

End-to-end spatial transcriptomics analysis with Squidpy and Scanpy involves preprocessing Visium or Slide-seq data, detecting spatially variable genes, identifying tissue domains, performing cell-type deconvolution, and examining spatial ligand-receptor interactions.

Yes, cell2location performs cell-type deconvolution in spatial spots by integrating spatial transcriptomics data with reference scRNA-seq datasets to estimate the abundance of distinct cell types across tissue locations.

Identifying spatial domains and spatially variable genes is best achieved using SpatialDE, BayesSpace, or STAGATE within a Squidpy workflow, which detect gene expression patterns and map tissue architecture automatically.

Yes, this spatial transcriptomics workflow supports Visium, MERFISH, and Slide-seq data formats, allowing you to preprocess spatial data and perform quality control and normalization across these common spatial library platforms.

Analyzing spatial ligand-receptor interactions is done by computing proximity metrics within a Squidpy workflow to examine inter-domain signaling and identify cell-cell communication patterns across identified tissue domains.

STAGATE and BayesSpace are specialized for spatial domain discovery, but their limitations include requiring careful parameter tuning for different tissue types and potential computational intensity when processing high-resolution spatial transcriptomics datasets.