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  "Title": "CEllular Latent Dirichlet Allocation",
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  "Description": "Celda is a suite of Bayesian hierarchical models for\nclustering single-cell RNA-sequencing (scRNA-seq) data. It is\nable to perform \"bi-clustering\" and simultaneously cluster\ngenes into gene modules and cells into cell subpopulations. It\nalso contains DecontX, a novel Bayesian method to\ncomputationally estimate and remove RNA contamination in\nindividual cells without empty droplet information. A variety\nof scRNA-seq data visualization functions is also included.",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:50:13 UTC",
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  "_assets": [
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    "availableModels",
    "bestLogLikelihood",
    "celda",
    "celda_C",
    "celda_CG",
    "celda_G",
    "celdaClusters",
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    "celdaPerplexity",
    "celdaProbabilityMap",
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    "celdaTsne",
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    "clusterProbability",
    "compareCountMatrix",
    "countChecksum",
    "decontX",
    "decontXcounts",
    "decontXcounts<-",
    "distinctColors",
    "factorizeMatrix",
    "featureModuleLookup",
    "featureModuleTable",
    "findMarkersTree",
    "geneSetEnrich",
    "logLikelihood",
    "logLikelihoodHistory",
    "matrixNames",
    "moduleHeatmap",
    "normalizeCounts",
    "params",
    "perplexity",
    "plotCeldaViolin",
    "plotDecontXContamination",
    "plotDecontXMarkerExpression",
    "plotDecontXMarkerPercentage",
    "plotDendro",
    "plotDimReduceCluster",
    "plotDimReduceFeature",
    "plotDimReduceGrid",
    "plotDimReduceModule",
    "plotGridSearchPerplexity",
    "plotHeatmap",
    "plotMarkerHeatmap",
    "plotRPC",
    "recodeClusterY",
    "recodeClusterZ",
    "recursiveSplitCell",
    "recursiveSplitModule",
    "reorderCelda",
    "reportCeldaCGPlotResults",
    "reportCeldaCGRun",
    "resamplePerplexity",
    "resList",
    "retrieveFeatureIndex",
    "runParams",
    "sampleLabel",
    "sampleLabel<-",
    "selectBestModel",
    "selectFeatures",
    "simulateCells",
    "simulateContamination",
    "splitModule",
    "subsetCeldaList",
    "topRank"
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      "title": "celdaCGGridSearchRes",
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      "class": [
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      "fields": [],
      "table": false,
      "tojson": false
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      "title": "celdaCGmod",
      "object": "celdaCGMod",
      "class": [
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      "table": false,
      "tojson": false
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      "name": "celdaCGSim",
      "title": "celdaCGSim",
      "object": "celdaCGSim",
      "class": [
        "list"
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      "fields": [],
      "table": false,
      "tojson": true
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      "name": "celdaCMod",
      "title": "celdaCMod",
      "object": "celdaCMod",
      "class": [
        "celda_C"
      ],
      "fields": [],
      "table": false,
      "tojson": false
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      "name": "celdaCSim",
      "title": "celdaCSim",
      "object": "celdaCSim",
      "class": [
        "list"
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      "fields": [],
      "table": false,
      "tojson": true
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      "name": "celdaGMod",
      "title": "celdaGMod",
      "object": "celdaGMod",
      "class": [
        "celda_G"
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      "name": "celdaGSim",
      "title": "celdaGSim",
      "object": "celdaGSim",
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      "tojson": true
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      "tojson": true
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        "array"
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      "title": "sceCeldaCG",
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      "table": false,
      "tojson": false
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      "page": "appendCeldaList",
      "title": "Append two celdaList objects",
      "topics": [
        "appendCeldaList"
      ]
    },
    {
      "page": "availableModels",
      "title": "available models",
      "topics": [
        "availableModels"
      ]
    },
    {
      "page": "bestLogLikelihood",
      "title": "Get the log-likelihood",
      "topics": [
        "bestLogLikelihood",
        "bestLogLikelihood,celdaModel-method",
        "bestLogLikelihood,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celda",
      "title": "Celda models",
      "topics": [
        "celda"
      ]
    },
    {
      "page": "celda_C",
      "title": "Cell clustering with Celda",
      "topics": [
        "celda_C",
        "celda_C,ANY-method",
        "celda_C,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celda_CG",
      "title": "Cell and feature clustering with Celda",
      "topics": [
        "celda_CG",
        "celda_CG,ANY-method",
        "celda_CG,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celda_G",
      "title": "Feature clustering with Celda",
      "topics": [
        "celda_G",
        "celda_G,ANY-method",
        "celda_G,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaCGGridSearchRes",
      "title": "celdaCGGridSearchRes",
      "topics": [
        "celdaCGGridSearchRes"
      ]
    },
    {
      "page": "celdaCGMod",
      "title": "celdaCGmod",
      "topics": [
        "celdaCGMod"
      ]
    },
    {
      "page": "celdaCGSim",
      "title": "celdaCGSim",
      "topics": [
        "celdaCGSim"
      ]
    },
    {
      "page": "celdaClusters",
      "title": "Get or set the cell cluster labels from a celda SingleCellExperiment object or celda model object.",
      "topics": [
        "celdaClusters",
        "celdaClusters,celdaModel-method",
        "celdaClusters,SingleCellExperiment-method",
        "celdaClusters<-",
        "celdaClusters<-,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaCMod",
      "title": "celdaCMod",
      "topics": [
        "celdaCMod"
      ]
    },
    {
      "page": "celdaCSim",
      "title": "celdaCSim",
      "topics": [
        "celdaCSim"
      ]
    },
    {
      "page": "celdaGMod",
      "title": "celdaGMod",
      "topics": [
        "celdaGMod"
      ]
    },
    {
      "page": "celdaGridSearch",
      "title": "Run Celda in parallel with multiple parameters",
      "topics": [
        "celdaGridSearch",
        "celdaGridSearch,matrix-method",
        "celdaGridSearch,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaGSim",
      "title": "celdaGSim",
      "topics": [
        "celdaGSim"
      ]
    },
    {
      "page": "celdaHeatmap",
      "title": "Plot celda Heatmap",
      "topics": [
        "celdaHeatmap",
        "celdaHeatmap,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaModel",
      "title": "Get celda model from a celda SingleCellExperiment object",
      "topics": [
        "celdaModel",
        "celdaModel,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaModules",
      "title": "Get or set the feature module labels from a celda SingleCellExperiment object.",
      "topics": [
        "celdaModules",
        "celdaModules,SingleCellExperiment-method",
        "celdaModules<-",
        "celdaModules<-,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaPerplexity",
      "title": "Get perplexity for every model in a celdaList",
      "topics": [
        "celdaPerplexity"
      ]
    },
    {
      "page": "celdaPerplexity-celdaList-method",
      "title": "Get perplexity for every model in a celdaList",
      "topics": [
        "celdaPerplexity,celdaList-method"
      ]
    },
    {
      "page": "celdaProbabilityMap",
      "title": "Probability map for a celda model",
      "topics": [
        "celdaProbabilityMap",
        "celdaProbabilityMap,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdatosce",
      "title": "Convert old celda model object to 'SCE' object",
      "topics": [
        "celdatosce",
        "celdatosce,celdaList-method",
        "celdatosce,celda_C-method",
        "celdatosce,celda_CG-method",
        "celdatosce,celda_G-method"
      ]
    },
    {
      "page": "celdaTsne",
      "title": "t-Distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction for celda 'sce' object",
      "topics": [
        "celdaTsne",
        "celdaTsne,SingleCellExperiment-method"
      ]
    },
    {
      "page": "celdaUmap",
      "title": "Uniform Manifold Approximation and Projection (UMAP) dimension reduction for celda 'sce' object",
      "topics": [
        "celdaUmap",
        "celdaUmap,SingleCellExperiment-method"
      ]
    },
    {
      "page": "clusterProbability",
      "title": "Get the conditional probabilities of cell in subpopulations from celda model",
      "topics": [
        "clusterProbability",
        "clusterProbability,SingleCellExperiment-method"
      ]
    },
    {
      "page": "compareCountMatrix",
      "title": "Check count matrix consistency",
      "topics": [
        "compareCountMatrix",
        "compareCountMatrix,ANY,celdaList-method",
        "compareCountMatrix,ANY,celdaModel-method"
      ]
    },
    {
      "page": "contaminationSim",
      "title": "contaminationSim",
      "topics": [
        "contaminationSim"
      ]
    },
    {
      "page": "countChecksum",
      "title": "Get the MD5 hash of the count matrix from the celdaList",
      "topics": [
        "countChecksum"
      ]
    },
    {
      "page": "countChecksum-celdaList-method",
      "title": "Get the MD5 hash of the count matrix from the celdaList",
      "topics": [
        "countChecksum,celdaList-method"
      ]
    },
    {
      "page": "decontX",
      "title": "Contamination estimation with decontX",
      "topics": [
        "decontX",
        "decontX,ANY-method",
        "decontX,SingleCellExperiment-method"
      ]
    },
    {
      "page": "decontXcounts",
      "title": "Get or set decontaminated counts matrix",
      "topics": [
        "decontXcounts",
        "decontXcounts,SingleCellExperiment-method",
        "decontXcounts<-",
        "decontXcounts<-,SingleCellExperiment-method"
      ]
    },
    {
      "page": "distinctColors",
      "title": "Create a color palette",
      "topics": [
        "distinctColors"
      ]
    },
    {
      "page": "eigenMatMultInt",
      "title": "Fast matrix multiplication for double x int",
      "topics": [
        "eigenMatMultInt"
      ]
    },
    {
      "page": "eigenMatMultNumeric",
      "title": "Fast matrix multiplication for double x double",
      "topics": [
        "eigenMatMultNumeric"
      ]
    },
    {
      "page": "factorizeMatrix",
      "title": "Generate factorized matrices showing each feature's influence on cell / gene clustering",
      "topics": [
        "factorizeMatrix",
        "factorizeMatrix,ANY,celda_C-method",
        "factorizeMatrix,ANY,celda_CG-method",
        "factorizeMatrix,ANY,celda_G-method",
        "factorizeMatrix,SingleCellExperiment,ANY-method"
      ]
    },
    {
      "page": "fastNormProp",
      "title": "Fast normalization for numeric matrix",
      "topics": [
        "fastNormProp"
      ]
    },
    {
      "page": "fastNormPropLog",
      "title": "Fast normalization for numeric matrix",
      "topics": [
        "fastNormPropLog"
      ]
    },
    {
      "page": "fastNormPropSqrt",
      "title": "Fast normalization for numeric matrix",
      "topics": [
        "fastNormPropSqrt"
      ]
    },
    {
      "page": "featureModuleLookup",
      "title": "Obtain the gene module of a gene of interest",
      "topics": [
        "featureModuleLookup",
        "featureModuleLookup,SingleCellExperiment-method"
      ]
    },
    {
      "page": "featureModuleTable",
      "title": "Output a feature module table",
      "topics": [
        "featureModuleTable"
      ]
    },
    {
      "page": "findMarkersTree",
      "title": "Generate marker decision tree from single-cell clustering output",
      "topics": [
        "findMarkersTree"
      ]
    },
    {
      "page": "geneSetEnrich",
      "title": "Gene set enrichment",
      "topics": [
        "geneSetEnrich",
        "geneSetEnrich,matrix-method",
        "geneSetEnrich,SingleCellExperiment-method"
      ]
    },
    {
      "page": "getDecisions",
      "title": "Gets cluster estimates using rules generated by `celda::findMarkersTree`",
      "topics": [
        "getDecisions"
      ]
    },
    {
      "page": "logLikelihood",
      "title": "Calculate the Log-likelihood of a celda model",
      "topics": [
        "logLikelihood",
        "logLikelihood,matrix,celda_C-method",
        "logLikelihood,matrix,celda_CG-method",
        "logLikelihood,matrix,celda_G-method",
        "logLikelihood,SingleCellExperiment,ANY-method"
      ]
    },
    {
      "page": "logLikelihoodHistory",
      "title": "Get log-likelihood history",
      "topics": [
        "logLikelihoodHistory",
        "logLikelihoodHistory,celdaModel-method",
        "logLikelihoodHistory,SingleCellExperiment-method"
      ]
    },
    {
      "page": "matrixNames",
      "title": "Get feature, cell and sample names from a celdaModel",
      "topics": [
        "matrixNames",
        "matrixNames,celdaModel-method"
      ]
    },
    {
      "page": "moduleHeatmap",
      "title": "Heatmap for featureModules",
      "topics": [
        "moduleHeatmap",
        "moduleHeatmap,SingleCellExperiment-method"
      ]
    },
    {
      "page": "nonzero",
      "title": "get row and column indices of none zero elements in the matrix",
      "topics": [
        "nonzero"
      ]
    },
    {
      "page": "normalizeCounts",
      "title": "Normalization of count data",
      "topics": [
        "normalizeCounts"
      ]
    },
    {
      "page": "params",
      "title": "Get parameter values provided for celdaModel creation",
      "topics": [
        "params",
        "params,celdaModel-method"
      ]
    },
    {
      "page": "perplexity",
      "title": "Calculate the perplexity of a celda model",
      "topics": [
        "perplexity",
        "perplexity,ANY,celda_C-method",
        "perplexity,ANY,celda_CG-method",
        "perplexity,ANY,celda_G-method",
        "perplexity,SingleCellExperiment,ANY-method"
      ]
    },
    {
      "page": "plotCeldaViolin",
      "title": "Feature Expression Violin Plot",
      "topics": [
        "plotCeldaViolin",
        "plotCeldaViolin,ANY-method",
        "plotCeldaViolin,SingleCellExperiment-method"
      ]
    },
    {
      "page": "plotDecontXContamination",
      "title": "Plots contamination on UMAP coordinates",
      "topics": [
        "plotDecontXContamination"
      ]
    },
    {
      "page": "plotDecontXMarkerExpression",
      "title": "Plots expression of marker genes before and after decontamination",
      "topics": [
        "plotDecontXMarkerExpression"
      ]
    },
    {
      "page": "plotDecontXMarkerPercentage",
      "title": "Plots percentage of cells cell types expressing markers",
      "topics": [
        "plotDecontXMarkerPercentage"
      ]
    },
    {
      "page": "plotDendro",
      "title": "Plots dendrogram of _findMarkersTree_ output",
      "topics": [
        "plotDendro"
      ]
    },
    {
      "page": "plotDimReduceCluster",
      "title": "Plotting the cell labels on a dimension reduction plot",
      "topics": [
        "plotDimReduceCluster",
        "plotDimReduceCluster,SingleCellExperiment-method",
        "plotDimReduceCluster,vector-method"
      ]
    },
    {
      "page": "plotDimReduceFeature",
      "title": "Plotting feature expression on a dimension reduction plot",
      "topics": [
        "plotDimReduceFeature",
        "plotDimReduceFeature,ANY-method",
        "plotDimReduceFeature,SingleCellExperiment-method"
      ]
    },
    {
      "page": "plotDimReduceGrid",
      "title": "Mapping the dimension reduction plot",
      "topics": [
        "plotDimReduceGrid",
        "plotDimReduceGrid,ANY-method",
        "plotDimReduceGrid,SingleCellExperiment-method"
      ]
    },
    {
      "page": "plotDimReduceModule",
      "title": "Plotting Celda module probability on a dimension reduction plot",
      "topics": [
        "plotDimReduceModule",
        "plotDimReduceModule,ANY-method",
        "plotDimReduceModule,SingleCellExperiment-method"
      ]
    },
    {
      "page": "plotGridSearchPerplexity",
      "title": "Visualize perplexity of a list of celda models",
      "topics": [
        "plotGridSearchPerplexity",
        "plotGridSearchPerplexity,celdaList-method",
        "plotGridSearchPerplexity,SingleCellExperiment-method"
      ]
    },
    {
      "page": "plotHeatmap",
      "title": "Plots heatmap based on Celda model",
      "topics": [
        "plotHeatmap"
      ]
    },
    {
      "page": "plotMarkerHeatmap",
      "title": "Generate heatmap for a marker decision tree",
      "topics": [
        "plotMarkerHeatmap"
      ]
    },
    {
      "page": "plotRPC",
      "title": "Visualize perplexity differences of a list of celda models",
      "topics": [
        "plotRPC",
        "plotRPC,celdaList-method",
        "plotRPC,SingleCellExperiment-method"
      ]
    },
    {
      "page": "recodeClusterY",
      "title": "Recode feature module labels",
      "topics": [
        "recodeClusterY"
      ]
    },
    {
      "page": "recodeClusterZ",
      "title": "Recode cell cluster labels",
      "topics": [
        "recodeClusterZ"
      ]
    },
    {
      "page": "recursiveSplitCell",
      "title": "Recursive cell splitting",
      "topics": [
        "recursiveSplitCell",
        "recursiveSplitCell,matrix-method",
        "recursiveSplitCell,SingleCellExperiment-method"
      ]
    },
    {
      "page": "recursiveSplitModule",
      "title": "Recursive module splitting",
      "topics": [
        "recursiveSplitModule",
        "recursiveSplitModule,matrix-method",
        "recursiveSplitModule,SingleCellExperiment-method"
      ]
    },
    {
      "page": "reorderCelda",
      "title": "Reorder cells populations and/or features modules using hierarchical clustering",
      "topics": [
        "reorderCelda",
        "reorderCelda,matrix,celda_C-method",
        "reorderCelda,matrix,celda_CG-method",
        "reorderCelda,matrix,celda_G-method",
        "reorderCelda,SingleCellExperiment,ANY-method"
      ]
    },
    {
      "page": "reportceldaCG",
      "title": "Generate an HTML report for celda_CG",
      "topics": [
        "reportceldaCG",
        "reportCeldaCGPlotResults",
        "reportCeldaCGRun"
      ]
    },
    {
      "page": "resamplePerplexity",
      "title": "Calculate and visualize perplexity of all models in a celdaList",
      "topics": [
        "resamplePerplexity",
        "resamplePerplexity,ANY-method",
        "resamplePerplexity,SingleCellExperiment-method"
      ]
    },
    {
      "page": "resList",
      "title": "Get final celdaModels from a celda model 'SCE' or celdaList object",
      "topics": [
        "resList",
        "resList,celdaList-method",
        "resList,SingleCellExperiment-method"
      ]
    },
    {
      "page": "retrieveFeatureIndex",
      "title": "Retrieve row index for a set of features",
      "topics": [
        "retrieveFeatureIndex"
      ]
    },
    {
      "page": "runParams",
      "title": "Get run parameters from a celda model 'SingleCellExperiment' or 'celdaList' object",
      "topics": [
        "runParams",
        "runParams,celdaList-method",
        "runParams,SingleCellExperiment-method"
      ]
    },
    {
      "page": "sampleCells",
      "title": "sampleCells",
      "topics": [
        "sampleCells"
      ]
    },
    {
      "page": "sampleLabel",
      "title": "Get or set sample labels from a celda SingleCellExperiment object",
      "topics": [
        "sampleLabel",
        "sampleLabel,celdaModel-method",
        "sampleLabel,SingleCellExperiment-method",
        "sampleLabel<-",
        "sampleLabel<-,SingleCellExperiment-method"
      ]
    },
    {
      "page": "sceCeldaC",
      "title": "sceCeldaC",
      "topics": [
        "sceCeldaC"
      ]
    },
    {
      "page": "sceCeldaCG",
      "title": "sceCeldaCG",
      "topics": [
        "sceCeldaCG"
      ]
    },
    {
      "page": "sceCeldaCGGridSearch",
      "title": "sceCeldaCGGridSearch",
      "topics": [
        "sceCeldaCGGridSearch"
      ]
    },
    {
      "page": "sceCeldaG",
      "title": "sceCeldaG",
      "topics": [
        "sceCeldaG"
      ]
    },
    {
      "page": "selectBestModel",
      "title": "Select best chain within each combination of parameters",
      "topics": [
        "selectBestModel",
        "selectBestModel,celdaList-method",
        "selectBestModel,SingleCellExperiment-method"
      ]
    },
    {
      "page": "selectFeatures",
      "title": "Simple feature selection by feature counts",
      "topics": [
        "selectFeatures",
        "selectFeatures,matrix-method",
        "selectFeatures,SingleCellExperiment-method"
      ]
    },
    {
      "page": "semiPheatmap",
      "title": "A function to draw clustered heatmaps.",
      "topics": [
        "semiPheatmap"
      ]
    },
    {
      "page": "simulateCells",
      "title": "Simulate count data from the celda generative models.",
      "topics": [
        "simulateCells"
      ]
    },
    {
      "page": "simulateContamination",
      "title": "Simulate contaminated count matrix",
      "topics": [
        "simulateContamination"
      ]
    },
    {
      "page": "splitModule",
      "title": "Split celda feature module",
      "topics": [
        "splitModule",
        "splitModule,SingleCellExperiment-method"
      ]
    },
    {
      "page": "subsetCeldaList",
      "title": "Subset celda model from SCE object returned from 'celdaGridSearch'",
      "topics": [
        "subsetCeldaList",
        "subsetCeldaList,celdaList-method",
        "subsetCeldaList,SingleCellExperiment-method"
      ]
    },
    {
      "page": "topRank",
      "title": "Identify features with the highest influence on clustering.",
      "topics": [
        "topRank"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/celda/raw/HEAD/README.md",
  "_rundeps": [
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    "MCMCprecision",
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    "pheatmap",
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    "pROC",
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    "RColorBrewer",
    "Rcpp",
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  "_sysdeps": [
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      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "14.2.0-4ubuntu2~24.04.1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
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      "shlib": "libgomp",
      "package": "libgomp1",
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      "name": "openmp",
      "homepage": "http://gcc.gnu.org/",
      "description": "GCC OpenMP (GOMP) support library"
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  "_vignettes": [
    {
      "source": "celda.Rmd",
      "filename": "celda.html",
      "title": "Analysis of single-cell genomic data with celda",
      "author": "Joshua Campbell, Zhe Wang, Shiyi Yang, Sean Corbett, Yusuke Koga",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "Generation of a simulated single cell dataset",
        "Feature selection",
        "Performing bi-clustering with celda",
        "Visualization",
        "Plotting cell populations on 2D-embeddings",
        "Creating an expression heatmap",
        "Displaying relationships between modules and cell populations",
        "Examining co-expression with module heatmaps",
        "Identifying reasonable numbers of feature modules and cell subpopulations",
        "Using recursive splitting",
        "Using a grid search",
        "Miscellaneous utility functions",
        "Finding the modules for feature with featureModuleLookup",
        "Reordering cluster labels with recodeClusterZ, recodeClusterY",
        "Session Information"
      ],
      "created": "2020-03-16 04:43:10",
      "modified": "2021-09-30 01:14:49",
      "commits": 9
    },
    {
      "source": "decontX.Rmd",
      "filename": "decontX.html",
      "title": "Decontamination of ambient RNA in single-cell genomic data with DecontX",
      "author": "Shiyi (Iris) Yang, Zhe Wang, Yuan Yin, Joshua Campbell",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Importing data",
        "Load PBMC4k data from 10X",
        "Running decontX",
        "Plotting DecontX results",
        "Cluster labels on UMAP",
        "Contamination on UMAP",
        "Expression of markers on UMAP",
        "Barplot of markers detected in cell clusters",
        "Violin plot to compare the distributions of original and decontaminated counts",
        "Other important notes",
        "Choosing appropriate cell clusters",
        "Adjusting the priors to influence contamination estimates",
        "Working with Seurat",
        "Session Information"
      ],
      "created": "2020-03-16 04:43:10",
      "modified": "2022-04-13 19:55:44",
      "commits": 18
    }
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  "_indexed": true,
  "_nocasepkg": "celda",
  "_universes": [
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