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| appendCeldaList | Append two celdaList objects |
| availableModels | available models |
| bestLogLikelihood | Get the log-likelihood |
| bestLogLikelihood-method | Get the log-likelihood |
| celda | Celda models |
| celdaCGGridSearchRes | celdaCGGridSearchRes |
| celdaCGMod | celdaCGmod |
| celdaCGSim | celdaCGSim |
| celdaCMod | celdaCMod |
| celdaCSim | celdaCSim |
| celdaGMod | celdaGMod |
| celdaGridSearch | Run Celda in parallel with multiple parameters |
| celdaGSim | celdaGSim |
| celdaHeatmap | Plot celda Heatmap |
| celdaHeatmap-method | Heatmap for celda_C |
| celdaHeatmap-method | Heatmap for celda_CG |
| celdaHeatmap-method | Heatmap for celda_CG |
| celdaPerplexity | Get perplexity for every model in a celdaList |
| celdaPerplexity-method | Get perplexity for every model in a celdaList |
| celdaProbabilityMap | Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
| celdaProbabilityMap-method | Probability map for a celda_C model |
| celdaProbabilityMap-method | Probability map for a celda_CG model |
| celdaTsne | Embeds cells in two dimensions using tSNE based on celda_CG results. |
| celdaTsne-method | tSNE for celda_C |
| celdaTsne-method | tSNE for celda_CG |
| celdaTsne-method | tSNE for celda_G |
| celdaUmap | Embeds cells in two dimensions using umap. |
| celdaUmap-method | umap for celda_C |
| celdaUmap-method | umap for celda_CG |
| celdaUmap-method | umap for celda_G |
| celda_C | Cell clustering with Celda |
| celda_CG | Cell and feature clustering with Celda |
| celda_G | Feature clustering with Celda |
| clusterProbability | Get cluster probability |
| clusterProbability-method | Conditional probabilities for cells in subpopulations from a Celda_C model |
| clusterProbability-method | Conditional probabilities for cells and features from a Celda_CG model |
| clusterProbability-method | Conditional probabilities for features in modules from a Celda_G model |
| clusters | Get clustering outcomes from a celdaModel |
| clusters-method | Get clustering outcomes from a celdaModel |
| compareCountMatrix | Check count matrix consistency |
| contaminationSim | contaminationSim |
| countChecksum | Get the MD5 hash of the count matrix from the celdaList |
| countChecksum-method | Get the MD5 hash of the count matrix from the celdaList |
| decontX | Contamination estimation with decontX |
| decontX-method | Contamination estimation with decontX |
| decontXcounts | Get or set decontaminated counts matrix |
| decontXcounts<- | Get or set decontaminated counts matrix |
| differentialExpression | Differential expression for cell subpopulations using MAST |
| distinctColors | Create a color palette |
| eigenMatMultInt | Fast matrix multiplication for double x int |
| factorizeMatrix | Generate factorized matrices showing each feature's influence on cell / gene clustering |
| factorizeMatrix-method | Matrix factorization for results from celda_C() |
| factorizeMatrix-method | Matrix factorization for results from celda_CG |
| factorizeMatrix-method | Matrix factorization for results from celda_G |
| fastNormProp | Fast normalization for numeric matrix |
| fastNormPropLog | Fast normalization for numeric matrix |
| fastNormPropSqrt | Fast normalization for numeric matrix |
| featureModuleLookup | Obtain the gene module of a gene of interest |
| featureModuleLookup-method | Lookup the module of a feature |
| featureModuleLookup-method | Lookup the module of a feature |
| featureModuleLookup-method | Lookup the module of a feature |
| featureModuleTable | Outputting a feature module table |
| findMarkersTree | Generate marker decision tree from single-cell clustering output |
| geneSetEnrich | Gene set enrichment |
| getDecisions | Gets cluster estimates using rules generated by 'celda::findMarkersTree' |
| logLikelihood | Calculate LogLikelihood |
| logLikelihoodcelda_C | Calculate Celda_C log likelihood |
| logLikelihoodcelda_CG | Calculate Celda_CG log likelihood |
| logLikelihoodcelda_G | Calculate Celda_G log likelihood |
| logLikelihoodHistory | Get log-likelihood history |
| logLikelihoodHistory-method | Get log-likelihood history |
| matrixNames | Get feature, cell and sample names from a celdaModel |
| matrixNames-method | Get feature, cell and sample names from a celdaModel |
| moduleHeatmap | Heatmap for featureModules |
| nonzero | get row and column indices of none zero elements in the matrix |
| normalizeCounts | Normalization of count data |
| params | Get parameter values provided for celdaModel creation |
| params-method | Get parameter values provided for celdaModel creation |
| perplexity | Calculate the perplexity from a single celdaModel |
| perplexity-method | Calculate the perplexity on new data with a celda_C model |
| perplexity-method | Calculate the perplexity on new data with a celda_CG model |
| perplexity-method | Calculate the perplexity on new data with a celda_G model |
| plotCeldaViolin | Feature Expression Violin Plot |
| plotDecontXContamination | Plots contamination on UMAP coordinates |
| plotDecontXMarkerExpression | Plots expression of marker genes before and after decontamination |
| plotDecontXMarkerPercentage | Plots percentage of cells cell types expressing markers |
| plotDimReduceCluster | Plotting the cell labels on a dimensionality reduction plot |
| plotDimReduceFeature | Plotting feature expression on a dimensionality reduction plot |
| plotDimReduceGrid | Mapping the dimensionality reduction plot |
| plotDimReduceModule | Plotting the Celda module probability on a dimensionality reduction plot |
| plotGridSearchPerplexity | Visualize perplexity of a list of celda models |
| plotGridSearchPerplexitycelda_C | Plot perplexity as a function of K from celda_C models |
| plotGridSearchPerplexitycelda_CG | Plot perplexity as a function of K and L from celda_CG models |
| plotGridSearchPerplexitycelda_G | Plot perplexity as a function of L from a celda_G model |
| plotHeatmap | Plots heatmap based on Celda model |
| plotMarkerDendro | Plots dendrogram of _findMarkersTree_ output |
| plotMarkerHeatmap | Generate heatmap for a marker decision tree |
| recodeClusterY | Recode feature module clusters |
| recodeClusterZ | Recode cell cluster labels |
| recursiveSplitCell | Recursive cell splitting |
| recursiveSplitModule | Recursive module splitting |
| resamplePerplexity | Calculate and visualize perplexity of all models in a celdaList, with count resampling |
| resList | Get final celdaModels from a celdaList |
| resList-method | Get final celdaModels from a celdaList |
| retrieveFeatureIndex | Retrieve row index for a set of features |
| runParams | Get run parameters provided to 'celdaGridSearch()' |
| runParams-method | Get run parameters provided to 'celdaGridSearch()' |
| sampleCells | sampleCells |
| sampleLabel | Get sampleLabels from a celdaModel |
| sampleLabel-method | Get sampleLabels from a celdaModel |
| selectBestModel | Select best chain within each combination of parameters |
| semiPheatmap | A function to draw clustered heatmaps. |
| simulateCells | Simulate count data from the celda generative models. |
| simulateCellscelda_C | Simulate cells from the celda_C model |
| simulateCellscelda_CG | Simulate cells from the celda_CG model |
| simulateCellscelda_G | Simulate cells from the celda_G model |
| simulateContamination | Simulate contaminated count matrix |
| subsetCeldaList | Subset celdaList object from celdaGridSearch |
| topRank | Identify features with the highest influence on clustering. |