Package: CelliD 1.15.0
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:
CelliD_1.15.0.tar.gz
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CelliD_1.15.0.tgz(r-4.4-x86_64)CelliD_1.15.0.tgz(r-4.4-arm64)CelliD_1.15.0.tgz(r-4.3-x86_64)CelliD_1.15.0.tgz(r-4.3-arm64)
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CelliD_1.15.0.tgz(r-4.4-emscripten)
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')) |
- Hallmark - Hallmark Pathways from MSigDB
- HgProteinCodingGenes - Homo Sapiens Protein Coding Genes
- MgProteinCodingGenes - Mus Musculus Protein Coding Genes
- seuratPbmc - Seurat object of 400 PBMC cells
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.
rnaseqsinglecelldimensionreductionclusteringgenesetenrichmentgeneexpressionatacseq
Last updated 25 days agofrom:af4d3e3a6d. Checks:OK: 1 WARNING: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win-x86_64 | WARNING | Oct 30 2024 |
| R-4.5-linux-x86_64 | WARNING | Oct 30 2024 |
| R-4.4-win-x86_64 | WARNING | Oct 30 2024 |
| R-4.4-mac-x86_64 | WARNING | Oct 30 2024 |
| R-4.4-mac-aarch64 | WARNING | Oct 30 2024 |
| R-4.3-win-x86_64 | WARNING | Oct 30 2024 |
| R-4.3-mac-x86_64 | WARNING | Oct 30 2024 |
| R-4.3-mac-aarch64 | WARNING | Oct 30 2024 |
Exports:DimPlotMCfgseaCelliDGetCellGeneRankingGetCellGeneSetGetGroupGeneRankingGetGroupGeneSetGetGSEAMatrixplotReducedDimMCRunCellGSEARunCellHGTRunGroupGSEARunMCARunMCDMAPRunMCTSNERunMCUMAP
Dependencies:abindaskpassassortheadbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsbslibcachemCairocaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfastmatchfgseafitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2ggrastrggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypheatmappillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6raggRANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsneS4ArraysS4VectorssassScaledMatrixscalesscaterscattermoresctransformscuttleSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsyssystemfontstensortextshapingtibbletictoctidyrtidyselecttinytexUCSC.utilsumaputf8uwotvctrsviporviridisviridisLitewithrxfunxtableXVectoryamlzlibbioczoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Multiple Correspondence Analysis on Single Cell for Joint Dimensionality Reduction of Gene and Cell, Cells Geneset Extraction and Geneset Enrichment Analysis | CelliD-package CelliD |
| Check for CelliD arguments | checkCelliDArg checkCelliDArg.Seurat checkCelliDArg.SingleCellExperiment |
| Seurat DimPlot for MCA like Dimensionality Reduction | DimPlotMC |
| Sort Gene Cell Distance Matrix | DistSort |
| Slight change in fgsea for ram and speed efficiency in CelliD | fgseaCelliD |
| Distance Calculation | GetCellGeneDistance GetCellGeneDistance.Seurat GetCellGeneDistance.SingleCellExperiment |
| Ranking Extraction | GetCellGeneRanking GetCellGeneRanking.Seurat GetCellGeneRanking.SingleCellExperiment |
| Gene sets extraction from MCA | GetCellGeneSet GetCellGeneSet.Seurat GetCellGeneSet.SingleCellExperiment |
| GeneCellCoordinates | GetGeneCellCoordinates |
| Centroids Coordinates | GetGroupCoordinates GetGroupCoordinates.matrix GetGroupCoordinates.Seurat GetGroupCoordinates.SingleCellExperiment |
| Centroids-Genes distances | GetGroupGeneDistance GetGroupGeneDistance.Seurat GetGroupGeneDistance.SingleCellExperiment |
| Gene Specificity Ranking Calculation | GetGroupGeneRanking GetGroupGeneRanking.Seurat GetGroupGeneRanking.SingleCellExperiment |
| Extract cluster/group gene sets from MCA | GetGroupGeneSet GetGroupGeneSet.Seurat GetGroupGeneSet.SingleCellExperiment |
| Get Matrix from Enrichment Results | GetGSEAMatrix |
| Hallmark Pathways from MSigDB | Hallmark |
| Homo Sapiens Protein Coding Genes | HgProteinCodingGenes |
| Import | import |
| Mus Musculus Protein Coding Genes | MgProteinCodingGenes |
| Distance Calculation | pairDist |
| Scater plotReducedDim for MCA like dimensionality Reduction | plotReducedDimMC |
| Run Gene Set Enrichment Analysis on cells | RunCellGSEA RunCellGSEA.Seurat RunCellGSEA.SingleCellExperiment |
| Run HyperGeometric Test on cells | RunCellHGT RunCellHGT.Seurat RunCellHGT.SingleCellExperiment |
| Run GSEA on cluster/groups | RunGroupGSEA RunGroupGSEA.Seurat RunGroupGSEA.SingleCellExperiment |
| Run Multiple Correspondence Analysis | RunMCA RunMCA.matrix RunMCA.Seurat RunMCA.SingleCellExperiment |
| Diffusion Map on MCA coordinates | RunMCDMAP RunMCDMAP.Seurat RunMCDMAP.SingleCellExperiment |
| tSNE on MCA coordinates | RunMCTSNE RunMCTSNE.Seurat RunMCTSNE.SingleCellExperiment |
| UMAP on MCA coordinates | RunMCUMAP RunMCUMAP.Seurat RunMCUMAP.SingleCellExperiment |
| SetDimSlot | setDimMCSlot setDimMCSlot.Seurat setDimMCSlot.SingleCellExperiment |
| Seurat object of 400 PBMC cells | seuratPbmc |