CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation
Calculate Accuracy of Predicted Cell Types
Add Gene Embedding to Seurat Object
Annotate Cells Using Distance Matrix and Marker Frequencies
Calculate Area Under the Curve (AUC) for Pathway Scores
Perform Cell Annotation Using CAESAR with Confidence and Proportion Ca...
Compute Co-embedding with Image Information Using CAESAR
Compute Co-embedding Using CAESAR
Test Cell Type Differentially Enriched Pathways
Test whether pathways are enriched
Calculate Spot Level Enrichment Scores for Pathways Using CAESAR
Perform Batch Correction and Integration with CAESAR Using Housekeepin...
Combine p-values Using the Cauchy Combination Method
Compute Spatial-Aware Cell Embeddings with Image Information
Compute Spatial-Aware Cell Embeddings with Image Information
Compute Spatial-Aware Cell Embeddings
Perform CAESAR embedding of Cells Using FAST with Spatial Weights
Plot Co-embedding UMAP for Genes and Cells
Co-embedding UMAP for Genes and Cells in a Seurat Object
Identify Signature Genes for Each Cell Type
getneighborhood_fast
Integrate Signature Genes Across Datasets
Select Marker Genes from a signature gene list Based on Expression Pro...
Convert Marker List to a Weighted Matrix
Calculate Signature Score for Cell Clusters
Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.
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