# CEllular Latent Dirichlet Allocation

## Documentation for package ‘celda’ version 1.4.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G L M N P R S T

## -- A --

appendCeldaList | Append two celdaList objects
availableModels | available models

## -- B --

bestLogLikelihood | Get the log-likelihood
bestLogLikelihood-method | Get the log-likelihood

## -- C --

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

## -- D --

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

## -- E --

eigenMatMultInt | Fast matrix multiplication for double x int

## -- F --

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

## -- G --

geneSetEnrich | Gene set enrichment
getDecisions | Gets cluster estimates using rules generated by 'celda::findMarkersTree'

## -- L --

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

## -- M --

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

## -- N --

nonzero | get row and column indices of none zero elements in the matrix
normalizeCounts | Normalization of count data

## -- P --

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

## -- R --

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()'

## -- S --

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

## -- T --

topRank | Identify features with the highest influence on clustering.