Package: CAESAR.Suite 0.1.0

Xiao Zhang

CAESAR.Suite: CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation

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.

Authors:Xiao Zhang [aut, cre], Wei Liu [aut], Jin Liu [aut]

CAESAR.Suite_0.1.0.tar.gz
CAESAR.Suite_0.1.0.tar.gz(r-4.5-noble)CAESAR.Suite_0.1.0.tar.gz(r-4.4-noble)
CAESAR.Suite_0.1.0.tgz(r-4.4-emscripten)
CAESAR.Suite.pdf |CAESAR.Suite.html
CAESAR.Suite/json (API)

# Install 'CAESAR.Suite' in R:
install.packages('CAESAR.Suite', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/xiaozhangryy/caesar.suite/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

3.00 score 2 scripts 158 downloads 22 exports 264 dependencies

Last updated 3 months agofrom:574e3b3c9a. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-linux-x86_64WARNINGNov 16 2024

Exports:accadd.gene.embeddingannotation_mataucCAESAR.annotationCAESAR.coembeddingCAESAR.coembedding.imageCAESAR.CTDEPCAESAR.enrich.pathwayCAESAR.enrich.scoreCAESAR.RUVCauchy.Combinationcellembedding_matrixcellembedding_seuratCoUMAPCoUMAP.plotfind.sig.genesgetneighborhood_fastcppIntsgmarker.selectmarkerList2matSigScore

Dependencies:abindade4AnnotationDbiaskpassassortheadbackportsbase64encbeachmatbeeswarmBHBiobaseBiocFileCacheBiocGenericsBiocNeighborsBiocParallelBiocSingularbiomaRtBiostringsbitbit64bitopsblobbootbroombslibcachemCairocarcarDatacaToolscellrangerclassclicliprclustercodetoolscolorspacecommonmarkCompQuadFormcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArraydeldirDerivDescToolsdigestdoBydotCall64dplyrdqrngDR.SCe1071evaluateExactexpmfansifarverfastDummiesfastmapfilelockfitdistrplusFNNfontawesomeforcatsformatRFormulafsfurrrfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2ggpubrggrastrggrepelggridgesggsciggsignifggthemesGiRaFgldglobalsgluegoftestgplotsgridExtragtablegtoolsharmonyhavenherehighrhmshtmltoolshtmlwidgetshttpuvhttrhttr2icaigraphIRangesirlbaisobandjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelistenvlme4lmomlmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmclustmemoisemgcvmicrobenchmarkmimeminiUIminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivopensslorg.Hs.eg.dborg.Mm.eg.dbparallellypatchworkpbapplypbkrtestpheatmappillarpixmappkgconfigplogrplotlyplyrpngpolyclippolynomPRECASTprettyunitsProFASTprogressprogressrpromisesproxypurrrquantregR6raggRANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppProgressRcppTOMLreadrreadxlrematchreshape2reticulateRhpcBLASctlrlangrmarkdownROCRrootSolverprojrootRSpectraRSQLiterstatixrstudioapirsvdRtsneS4ArraysS4VectorssassScaledMatrixscalesscaterscattermoresctransformscuttleSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraySparseMspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsyssystemfontstensortextshapingtibbletidyrtidyselecttinytextzdbUCSC.utilsutf8uwotvctrsviporviridisviridisLitevroomwithrxfunxml2xtableXVectoryamlzlibbioczoo

ST MOB data analysis

Rendered fromSTMOB.Rmdusingknitr::rmarkdownon Nov 16 2024.

Last update: 2024-09-17
Started: 2024-09-17

Xenium BC data analysis

Rendered fromXeniumBC.Rmdusingknitr::rmarkdownon Nov 16 2024.

Last update: 2024-09-17
Started: 2024-09-17

Readme and manuals

Help Manual

Help pageTopics
Calculate Accuracy of Predicted Cell Typesacc
Add Gene Embedding to Seurat Objectadd.gene.embedding
Annotate Cells Using Distance Matrix and Marker Frequenciesannotation_mat
Calculate Area Under the Curve (AUC) for Pathway Scoresauc
Perform Cell Annotation Using CAESAR with Confidence and Proportion CalculationCAESAR.annotation
Compute Co-embedding Using CAESARCAESAR.coembedding
Compute Co-embedding with Image Information Using CAESARCAESAR.coembedding.image
Test Cell Type Differentially Enriched PathwaysCAESAR.CTDEP
Test whether pathways are enrichedCAESAR.enrich.pathway
Calculate Spot Level Enrichment Scores for Pathways Using CAESARCAESAR.enrich.score
Perform Batch Correction and Integration with CAESAR Using Housekeeping GenesCAESAR.RUV
Combine p-values Using the Cauchy Combination MethodCauchy.Combination
Compute Spatial-Aware Cell Embeddings with Image Informationcellembedding_image_matrix
Compute Spatial-Aware Cell Embeddings with Image Informationcellembedding_image_seurat
Compute Spatial-Aware Cell Embeddingscellembedding_matrix
Perform CAESAR embedding of Cells Using FAST with Spatial Weightscellembedding_seurat
Co-embedding UMAP for Genes and Cells in a Seurat ObjectCoUMAP
Plot Co-embedding UMAP for Genes and CellsCoUMAP.plot
Identify Signature Genes for Each Cell Typefind.sig.genes
getneighborhood_fastgetneighborhood_fastcpp
Human housekeeping genes databaseHuman_HK_genes
Integrate Signature Genes Across DatasetsIntsg
Select Marker Genes from a signature gene list Based on Expression Proportion and Overlap Criteriamarker.select
Convert Marker List to a Weighted MatrixmarkerList2mat
Mouse housekeeping genes databaseMouse_HK_genes
Calculate Signature Score for Cell ClustersSigScore
A toy dataset to run examplestoydata