Function reference • celda

Primary celda functions Functions for clustering of cells
`celda_CG()`	Cell and feature clustering with Celda
`celda_C()`	Cell clustering with Celda
`celda_G()`	Feature clustering with Celda
`reportCeldaCGRun()` `reportCeldaCGPlotResults()`	Generate an HTML report for celda_CG
`selectFeatures()`	Simple feature selection by feature counts
`splitModule()`	Split celda feature module
Visualization functions for celda results Functions for displaying celda resuls on 2-D embeddings, heatmaps, and violin plots
`celdaUmap()`	Uniform Manifold Approximation and Projection (UMAP) dimension reduction for celda `sce` object
`celdaTsne()`	t-Distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction for celda `sce` object
`moduleHeatmap()`	Heatmap for featureModules
`celdaProbabilityMap()`	Probability map for a celda model
`plotDimReduceCluster()`	Plotting the cell labels on a dimension reduction plot
`plotDimReduceFeature()`	Plotting feature expression on a dimension reduction plot
`plotDimReduceModule()`	Plotting Celda module probability on a dimension reduction plot
`plotCeldaViolin()`	Feature Expression Violin Plot
`celdaHeatmap()`	Plot celda Heatmap
Primary decontX functions Functions for estimating and displaying contamination with decontX
`decontX()`	Contamination estimation with decontX
`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
`decontXcounts()` `decontXcounts<-`()	Get or set decontaminated counts matrix
Functions for determining the numbers of clusters in celda Functions for running and comparing multiple celda models with different number of modules or cell populations
`recursiveSplitCell()`	Recursive cell splitting
`recursiveSplitModule()`	Recursive module splitting
`plotRPC()`	Visualize perplexity differences of a list of celda models
`celdaGridSearch()`	Run Celda in parallel with multiple parameters
`plotGridSearchPerplexity()`	Visualize perplexity of a list of celda models
`perplexity()`	Calculate the perplexity of a celda model
`celdaPerplexity()`	Get perplexity for every model in a celdaList
`resamplePerplexity()`	Calculate and visualize perplexity of all models in a celdaList
`selectBestModel()`	Select best chain within each combination of parameters
`resList()`	Get final celdaModels from a celda model `SCE` or celdaList object
`subsetCeldaList()`	Subset celda model from SCE object returned from `celdaGridSearch`
`appendCeldaList()`	Append two celdaList objects
Miscellaneous celda functions Various functions for manipulation of celda results
`celdaClusters()` `celdaClusters<-`()	Get or set the cell cluster labels from a celda SingleCellExperiment object or celda model object.
`celdaModules()` `celdaModules<-`()	Get or set the feature module labels from a celda SingleCellExperiment object.
`recodeClusterY()`	Recode feature module labels
`recodeClusterZ()`	Recode cell cluster labels
`reorderCelda()`	Reorder cells populations and/or features modules using hierarchical clustering
`featureModuleLookup()`	Obtain the gene module of a gene of interest
`featureModuleTable()`	Output a feature module table
`celda()`	Celda models
`params()`	Get parameter values provided for celdaModel creation
`runParams()`	Get run parameters from a celda model `SingleCellExperiment` or `celdaList` object
`factorizeMatrix()`	Generate factorized matrices showing each feature's influence on cell / gene clustering
`bestLogLikelihood()`	Get the log-likelihood
`clusterProbability()`	Get the conditional probabilities of cell in subpopulations from celda model
`geneSetEnrich()`	Gene set enrichment
`plotHeatmap()`	Plots heatmap based on Celda model
`retrieveFeatureIndex()`	Retrieve row index for a set of features
`normalizeCounts()`	Normalization of count data
`distinctColors()`	Create a color palette
`matrixNames()`	Get feature, cell and sample names from a celdaModel
`logLikelihood()`	Calculate the Log-likelihood of a celda model
`logLikelihoodHistory()`	Get log-likelihood history
`topRank()`	Identify features with the highest influence on clustering.
`sampleLabel()` `sampleLabel<-`()	Get or set sample labels from a celda SingleCellExperiment object
Simulation functions Functions for generating data from the generative process of each model
`simulateCells()`	Simulate count data from the celda generative models.
`simulateContamination()`	Simulate contaminated count matrix
Data objects Small data objects used in examples
`sceCeldaCG`	sceCeldaCG
`sceCeldaC`	sceCeldaC
`sceCeldaG`	sceCeldaG
`sceCeldaCGGridSearch`	sceCeldaCGGridSearch
`celdaCGGridSearchRes`	celdaCGGridSearchRes
`sampleCells`	sampleCells
`contaminationSim`	contaminationSim

Reference

Primary celda functions

Visualization functions for celda results

Primary decontX functions

Functions for determining the numbers of clusters in celda

Miscellaneous celda functions

Simulation functions

Data objects