Package: CelliD 1.13.0

Akira Cortal

CelliD: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

Authors:Akira Cortal [aut, cre], Antonio Rausell [aut, ctb]

CelliD_1.13.0.tar.gz
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CelliD.pdf |CelliD.html✨
CelliD/json (API)
NEWS

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

Peer review:

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

On BioConductor:CelliD-1.13.0(bioc 3.20)CelliD-1.12.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

15 exports 0.71 score 191 dependencies

Last updated 2 months agofrom:de49f2ddc6

Exports:DimPlotMCfgseaCelliDGetCellGeneRankingGetCellGeneSetGetGroupGeneRankingGetGroupGeneSetGetGSEAMatrixplotReducedDimMCRunCellGSEARunCellHGTRunGroupGSEARunMCARunMCDMAPRunMCTSNERunMCUMAP

Dependencies:abindaskpassbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsbslibcachemCairocaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArrayDelayedMatrixStatsdeldirdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfastmatchfgseafitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2ggrastrggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypheatmappillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6raggRANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsneS4ArraysS4VectorssassScaledMatrixscalesscaterscattermoresctransformscuttleSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraysparseMatrixStatsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.utilsstringistringrSummarizedExperimentsurvivalsyssystemfontstensortextshapingtibbletictoctidyrtidyselecttinytexUCSC.utilsumaputf8uwotvctrsviporviridisviridisLitewithrxfunxtableXVectoryamlzlibbioczoo

CelliD Vignette

Akira Cortal, Antonio Rausell

Rendered fromBioconductorVignette.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2022-01-08
Started: 2021-01-11

Readme and manuals

Help Manual

Help pageTopics
Multiple Correspondence Analysis on Single Cell for Joint Dimensionality Reduction of Gene and Cell, Cells Geneset Extraction and Geneset Enrichment AnalysisCelliD-package CelliD
Check for CelliD argumentscheckCelliDArg checkCelliDArg.Seurat checkCelliDArg.SingleCellExperiment
Seurat DimPlot for MCA like Dimensionality ReductionDimPlotMC
Sort Gene Cell Distance MatrixDistSort
Slight change in fgsea for ram and speed efficiency in CelliDfgseaCelliD
Distance CalculationGetCellGeneDistance GetCellGeneDistance.Seurat GetCellGeneDistance.SingleCellExperiment
Ranking ExtractionGetCellGeneRanking GetCellGeneRanking.Seurat GetCellGeneRanking.SingleCellExperiment
Gene sets extraction from MCAGetCellGeneSet GetCellGeneSet.Seurat GetCellGeneSet.SingleCellExperiment
GeneCellCoordinatesGetGeneCellCoordinates
Centroids CoordinatesGetGroupCoordinates GetGroupCoordinates.matrix GetGroupCoordinates.Seurat GetGroupCoordinates.SingleCellExperiment
Centroids-Genes distancesGetGroupGeneDistance GetGroupGeneDistance.Seurat GetGroupGeneDistance.SingleCellExperiment
Gene Specificity Ranking CalculationGetGroupGeneRanking GetGroupGeneRanking.Seurat GetGroupGeneRanking.SingleCellExperiment
Extract cluster/group gene sets from MCAGetGroupGeneSet GetGroupGeneSet.Seurat GetGroupGeneSet.SingleCellExperiment
Get Matrix from Enrichment ResultsGetGSEAMatrix
Hallmark Pathways from MSigDBHallmark
Homo Sapiens Protein Coding GenesHgProteinCodingGenes
Importimport
Mus Musculus Protein Coding GenesMgProteinCodingGenes
Distance CalculationpairDist
Scater plotReducedDim for MCA like dimensionality ReductionplotReducedDimMC
Run Gene Set Enrichment Analysis on cellsRunCellGSEA RunCellGSEA.Seurat RunCellGSEA.SingleCellExperiment
Run HyperGeometric Test on cellsRunCellHGT RunCellHGT.Seurat RunCellHGT.SingleCellExperiment
Run GSEA on cluster/groupsRunGroupGSEA RunGroupGSEA.Seurat RunGroupGSEA.SingleCellExperiment
Run Multiple Correspondence AnalysisRunMCA RunMCA.matrix RunMCA.Seurat RunMCA.SingleCellExperiment
Diffusion Map on MCA coordinatesRunMCDMAP RunMCDMAP.Seurat RunMCDMAP.SingleCellExperiment
tSNE on MCA coordinatesRunMCTSNE RunMCTSNE.Seurat RunMCTSNE.SingleCellExperiment
UMAP on MCA coordinatesRunMCUMAP RunMCUMAP.Seurat RunMCUMAP.SingleCellExperiment
SetDimSlotsetDimMCSlot setDimMCSlot.Seurat setDimMCSlot.SingleCellExperiment
Seurat object of 400 PBMC cellsseuratPbmc