Package: celda 1.23.0
Joshua Campbell
celda: CEllular Latent Dirichlet Allocation
Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.
Authors:
celda_1.23.0.tar.gz
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celda.pdf |celda.html✨
celda/json (API)
NEWS
| # Install 'celda' in R: |
| install.packages('celda', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/campbio/celda/issues
- celdaCGGridSearchRes - CeldaCGGridSearchRes
- celdaCGMod - CeldaCGmod
- celdaCGSim - CeldaCGSim
- celdaCMod - CeldaCMod
- celdaCSim - CeldaCSim
- celdaGMod - CeldaGMod
- celdaGSim - CeldaGSim
- contaminationSim - ContaminationSim
- sampleCells - SampleCells
- sceCeldaC - SceCeldaC
- sceCeldaCG - SceCeldaCG
- sceCeldaCGGridSearch - SceCeldaCGGridSearch
- sceCeldaG - SceCeldaG
On BioConductor:celda-1.21.0(bioc 3.21)celda-1.22.0(bioc 3.20)
singlecellgeneexpressionclusteringsequencingbayesianimmunooncologydataimportcppopenmp
Last updated 2 months agofrom:73535bf3d9. Checks:OK: 3 NOTE: 5 ERROR: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 29 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 29 2024 |
| R-4.4-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-mac-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-mac-aarch64 | NOTE | Oct 30 2024 |
| R-4.3-win-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-aarch64 | ERROR | Oct 30 2024 |
Exports:appendCeldaListavailableModelsbestLogLikelihoodceldacelda_Ccelda_CGcelda_GceldaClustersceldaClusters<-celdaGridSearchceldaHeatmapceldaModelceldaModulesceldaModules<-celdaPerplexityceldaProbabilityMapceldatosceceldaTsneceldaUmapclusterProbabilitycompareCountMatrixcountChecksumdecontXdecontXcountsdecontXcounts<-distinctColorsfactorizeMatrixfeatureModuleLookupfeatureModuleTablegeneSetEnrichlogLikelihoodlogLikelihoodHistorymatrixNamesmoduleHeatmapnormalizeCountsparamsperplexityplotCeldaViolinplotDecontXContaminationplotDecontXMarkerExpressionplotDecontXMarkerPercentageplotDimReduceClusterplotDimReduceFeatureplotDimReduceGridplotDimReduceModuleplotGridSearchPerplexityplotHeatmapplotRPCrecodeClusterYrecodeClusterZrecursiveSplitCellrecursiveSplitModulereorderCeldareportCeldaCGPlotResultsreportCeldaCGRunresamplePerplexityresListretrieveFeatureIndexrunParamssampleLabelsampleLabel<-selectBestModelselectFeaturessimulateCellssimulateContaminationsplitModulesubsetCeldaListtopRank
Dependencies:abindaskpassassortheadbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblusterCairocirclizecliclueclustercodetoolscolorspacecombinatComplexHeatmapcpp11crayoncurldata.tabledbscanDelayedArraydigestdoParalleldqrngedgeRenrichRfansifarverFNNforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggbeeswarmggplot2ggrastrggrepelGlobalOptionsgluegridExtragtablehttrigraphIRangesirlbaisobanditeratorsjsonlitelabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsMCMCprecisionmetapodmgcvmimemunsellnlmeopensslpheatmappillarpkgconfigplyrpngR6raggRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppMLRcppProgressreshape2rjsonrlangRSpectrarsvdRtsneS4ArraysS4VectorsScaledMatrixscalesscaterscranscuttleshapeSingleCellExperimentsitmosnowSparseArraystatmodstringistringrSummarizedExperimentsyssystemfontstextshapingtibbleUCSC.utilsutf8uwotvctrsviporviridisviridisLitewithrWriteXLSXVectorzlibbioc
Analysis of single-cell genomic data with celda
Rendered fromcelda.Rmdusingknitr::rmarkdownon Nov 29 2024.Last update: 2021-09-30
Started: 2020-03-16
Decontamination of ambient RNA in single-cell genomic data with DecontX
Rendered fromdecontX.Rmdusingknitr::rmarkdownon Nov 29 2024.Last update: 2022-04-13
Started: 2020-03-16
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Append two celdaList objects | appendCeldaList |
| available models | availableModels |
| Get the log-likelihood | bestLogLikelihood bestLogLikelihood,celdaModel-method bestLogLikelihood,SingleCellExperiment-method |
| Celda models | celda |
| Cell clustering with Celda | celda_C celda_C,ANY-method celda_C,SingleCellExperiment-method |
| Cell and feature clustering with Celda | celda_CG celda_CG,ANY-method celda_CG,SingleCellExperiment-method |
| Feature clustering with Celda | celda_G celda_G,ANY-method celda_G,SingleCellExperiment-method |
| celdaCGGridSearchRes | celdaCGGridSearchRes |
| celdaCGmod | celdaCGMod |
| celdaCGSim | celdaCGSim |
| Get or set the cell cluster labels from a celda SingleCellExperiment object or celda model object. | celdaClusters celdaClusters,celdaModel-method celdaClusters,SingleCellExperiment-method celdaClusters<- celdaClusters<-,SingleCellExperiment-method |
| celdaCMod | celdaCMod |
| celdaCSim | celdaCSim |
| celdaGMod | celdaGMod |
| Run Celda in parallel with multiple parameters | celdaGridSearch celdaGridSearch,matrix-method celdaGridSearch,SingleCellExperiment-method |
| celdaGSim | celdaGSim |
| Plot celda Heatmap | celdaHeatmap celdaHeatmap,SingleCellExperiment-method |
| Get celda model from a celda SingleCellExperiment object | celdaModel celdaModel,SingleCellExperiment-method |
| Get or set the feature module labels from a celda SingleCellExperiment object. | celdaModules celdaModules,SingleCellExperiment-method celdaModules<- celdaModules<-,SingleCellExperiment-method |
| Get perplexity for every model in a celdaList | celdaPerplexity |
| Get perplexity for every model in a celdaList | celdaPerplexity,celdaList-method |
| Probability map for a celda model | celdaProbabilityMap celdaProbabilityMap,SingleCellExperiment-method |
| Convert old celda model object to 'SCE' object | celdatosce celdatosce,celdaList-method celdatosce,celda_C-method celdatosce,celda_CG-method celdatosce,celda_G-method |
| t-Distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction for celda 'sce' object | celdaTsne celdaTsne,SingleCellExperiment-method |
| Uniform Manifold Approximation and Projection (UMAP) dimension reduction for celda 'sce' object | celdaUmap celdaUmap,SingleCellExperiment-method |
| Get the conditional probabilities of cell in subpopulations from celda model | clusterProbability clusterProbability,SingleCellExperiment-method |
| Check count matrix consistency | compareCountMatrix compareCountMatrix,ANY,celdaList-method compareCountMatrix,ANY,celdaModel-method |
| contaminationSim | contaminationSim |
| Get the MD5 hash of the count matrix from the celdaList | countChecksum |
| Get the MD5 hash of the count matrix from the celdaList | countChecksum,celdaList-method |
| Contamination estimation with decontX | decontX decontX,ANY-method decontX,SingleCellExperiment-method |
| Get or set decontaminated counts matrix | decontXcounts decontXcounts,SingleCellExperiment-method decontXcounts<- decontXcounts<-,SingleCellExperiment-method |
| Create a color palette | distinctColors |
| Fast matrix multiplication for double x int | eigenMatMultInt |
| Fast matrix multiplication for double x double | eigenMatMultNumeric |
| Generate factorized matrices showing each feature's influence on cell / gene clustering | factorizeMatrix factorizeMatrix,ANY,celda_C-method factorizeMatrix,ANY,celda_CG-method factorizeMatrix,ANY,celda_G-method factorizeMatrix,SingleCellExperiment,ANY-method |
| Fast normalization for numeric matrix | fastNormProp |
| Fast normalization for numeric matrix | fastNormPropLog |
| Fast normalization for numeric matrix | fastNormPropSqrt |
| Obtain the gene module of a gene of interest | featureModuleLookup featureModuleLookup,SingleCellExperiment-method |
| Output a feature module table | featureModuleTable |
| Gene set enrichment | geneSetEnrich geneSetEnrich,matrix-method geneSetEnrich,SingleCellExperiment-method |
| Calculate the Log-likelihood of a celda model | logLikelihood logLikelihood,matrix,celda_C-method logLikelihood,matrix,celda_CG-method logLikelihood,matrix,celda_G-method logLikelihood,SingleCellExperiment,ANY-method |
| Get log-likelihood history | logLikelihoodHistory logLikelihoodHistory,celdaModel-method logLikelihoodHistory,SingleCellExperiment-method |
| Get feature, cell and sample names from a celdaModel | matrixNames matrixNames,celdaModel-method |
| Heatmap for featureModules | moduleHeatmap moduleHeatmap,SingleCellExperiment-method |
| get row and column indices of none zero elements in the matrix | nonzero |
| Normalization of count data | normalizeCounts |
| Get parameter values provided for celdaModel creation | params params,celdaModel-method |
| Calculate the perplexity of a celda model | perplexity perplexity,ANY,celda_C-method perplexity,ANY,celda_CG-method perplexity,ANY,celda_G-method perplexity,SingleCellExperiment,ANY-method |
| Feature Expression Violin Plot | plotCeldaViolin plotCeldaViolin,ANY-method plotCeldaViolin,SingleCellExperiment-method |
| Plots contamination on UMAP coordinates | plotDecontXContamination |
| Plots expression of marker genes before and after decontamination | plotDecontXMarkerExpression |
| Plots percentage of cells cell types expressing markers | plotDecontXMarkerPercentage |
| Plotting the cell labels on a dimension reduction plot | plotDimReduceCluster plotDimReduceCluster,SingleCellExperiment-method plotDimReduceCluster,vector-method |
| Plotting feature expression on a dimension reduction plot | plotDimReduceFeature plotDimReduceFeature,ANY-method plotDimReduceFeature,SingleCellExperiment-method |
| Mapping the dimension reduction plot | plotDimReduceGrid plotDimReduceGrid,ANY-method plotDimReduceGrid,SingleCellExperiment-method |
| Plotting Celda module probability on a dimension reduction plot | plotDimReduceModule plotDimReduceModule,ANY-method plotDimReduceModule,SingleCellExperiment-method |
| Visualize perplexity of a list of celda models | plotGridSearchPerplexity plotGridSearchPerplexity,celdaList-method plotGridSearchPerplexity,SingleCellExperiment-method |
| Plots heatmap based on Celda model | plotHeatmap |
| Visualize perplexity differences of a list of celda models | plotRPC plotRPC,celdaList-method plotRPC,SingleCellExperiment-method |
| Recode feature module labels | recodeClusterY |
| Recode cell cluster labels | recodeClusterZ |
| Recursive cell splitting | recursiveSplitCell recursiveSplitCell,matrix-method recursiveSplitCell,SingleCellExperiment-method |
| Recursive module splitting | recursiveSplitModule recursiveSplitModule,matrix-method recursiveSplitModule,SingleCellExperiment-method |
| Reorder cells populations and/or features modules using hierarchical clustering | reorderCelda reorderCelda,matrix,celda_C-method reorderCelda,matrix,celda_CG-method reorderCelda,matrix,celda_G-method reorderCelda,SingleCellExperiment,ANY-method |
| Generate an HTML report for celda_CG | reportceldaCG reportCeldaCGPlotResults reportCeldaCGRun |
| Calculate and visualize perplexity of all models in a celdaList | resamplePerplexity resamplePerplexity,ANY-method resamplePerplexity,SingleCellExperiment-method |
| Get final celdaModels from a celda model 'SCE' or celdaList object | resList resList,celdaList-method resList,SingleCellExperiment-method |
| Retrieve row index for a set of features | retrieveFeatureIndex |
| Get run parameters from a celda model 'SingleCellExperiment' or 'celdaList' object | runParams runParams,celdaList-method runParams,SingleCellExperiment-method |
| sampleCells | sampleCells |
| Get or set sample labels from a celda SingleCellExperiment object | sampleLabel sampleLabel,celdaModel-method sampleLabel,SingleCellExperiment-method sampleLabel<- sampleLabel<-,SingleCellExperiment-method |
| sceCeldaC | sceCeldaC |
| sceCeldaCG | sceCeldaCG |
| sceCeldaCGGridSearch | sceCeldaCGGridSearch |
| sceCeldaG | sceCeldaG |
| Select best chain within each combination of parameters | selectBestModel selectBestModel,celdaList-method selectBestModel,SingleCellExperiment-method |
| Simple feature selection by feature counts | selectFeatures selectFeatures,matrix-method selectFeatures,SingleCellExperiment-method |
| A function to draw clustered heatmaps. | semiPheatmap |
| Simulate count data from the celda generative models. | simulateCells |
| Simulate contaminated count matrix | simulateContamination |
| Split celda feature module | splitModule splitModule,SingleCellExperiment-method |
| Subset celda model from SCE object returned from 'celdaGridSearch' | subsetCeldaList subsetCeldaList,celdaList-method subsetCeldaList,SingleCellExperiment-method |
| Identify features with the highest influence on clustering. | topRank |
