Function Documentation

Man pages for all functions exported by celda

available_models

available models

calc.perplexity

calc.perplexity

calc.perplexity2

calc.perplexity2

calculateLoglikFromVariables.celda_C()

Calculate the celda_C log likelihood for user-provided cluster assignments

calculateLoglikFromVariables.celda_CG()

Calculate log lileklihood for the celda Cell and Gene clustering model, given a set of cell / gene cluster assignments

calculateLoglikFromVariables.celda_G()

Calculate the celda_G log likelihood for user-provided cluster assignments

calculateLoglikFromVariables()

Calculate a log-likelihood for a user-provided cluster assignment and count matrix, per the desired celda model.

calculatePerplexity(<celda_C>)

Calculate the perplexity from a single celda model

calculatePerplexity(<celda_CG>)

Calculate the perplexity from a single celda model

calculatePerplexity(<celda_G>)

Calculate the perplexity from a single celda model

calculatePerplexity()

Calculate the perplexity from a single celda model

calculatePerplexityWithResampling()

Calculate and visualize perplexity of all models in a celda_list, with count resampling

calculateTsne()

Run the t-SNE algorithm for dimensionality reduction

cC.decomposeCounts()

Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation

cCG.decomposeCounts()

Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation

celda()

Deprecation warning for old grid search function

celdaGridSearch()

Run the celda Bayesian hierarchical model on a matrix of counts.

celdaHeatmap(<celda_C>)

celdaHeatmap for celda Cell clustering function

celdaHeatmap(<celda_CG>)

celdaHeatmap for celda Cell and Gene clustering model.

celdaHeatmap(<celda_G>)

celdaHeatmap for celda Gene clustering model

celdaHeatmap()

Render a stylable heatmap of count data based on celda clustering results.

celdaProbabilityMap(<celda_C>)

Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations.

celdaProbabilityMap(<celda_CG>)

Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations.

celdaProbabilityMap()

Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations.

celdaTsne(<celda_C>)

Embeds cells in two dimensions using tSNE based on celda_C results.

celdaTsne(<celda_CG>)

Embeds cells in two dimensions using tSNE based on celda_CG results.

celdaTsne(<celda_G>)

Embeds cells in two dimensions using tSNE based on celda_CG results.

celdaTsne()

Embeds cells in two dimensions using tSNE based on celda_CG results.

celda_C()

celda Cell Clustering Model

celda_CG()

celda Cell and Gene Clustering Model

celda_G()

celda Gene Clustering Model

cG.decomposeCounts()

Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation

clusterProbability(<celda_C>)

Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments

clusterProbability(<celda_CG>)

Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments

clusterProbability(<celda_G>)

Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments

clusterProbability()

Get the probability of the cluster assignments generated during a celda run.

compareCountMatrix()

Check whether a count matrix was the one used in a given celda run

completeLogLikelihood()

Get the complete log likelihood for a given celda model.

differentialExpression()

Gene expression markers for cell clusters using MAST

distinct_colors()

Generate a distinct palette for coloring different clusters

eigenMatMultInt()

Fast matrix multiplication for double x int

factorizeMatrix(<celda_C>)

Generate factorized matrices showing each feature's influence on the celda_C model clustering

factorizeMatrix(<celda_CG>)

Generate factorized matrices showing each feature's influence on the celda_CG model clustering

factorizeMatrix(<celda_G>)

Generate factorized matrices showing each feature's influence on the celda_G model clustering

factorizeMatrix()

Generate factorized matrices showing each feature's influence on cell / gene clustering

fastNormProp()

Fast normalization for numeric matrix

fastNormPropLog()

Fast normalization for numeric matrix

fastNormPropSqrt()

Fast normalization for numeric matrix

featureModuleLookup(<celda_C>)

Obtain the gene module of a gene of interest

featureModuleLookup(<celda_CG>)

Obtain the feature module of a feature of interest

featureModuleLookup(<celda_G>)

Obtain the gene module of a gene of interest

featureModuleLookup()

Obtain the gene module of a gene of interest

finalLogLikelihood()

Get the log likelihood from the final iteration of Gibbs sampling for a given celda model.

moduleHeatmap()

Transcriptional state heatmap

normalizeCounts()

Performs normalization, transformation, and/or scaling on a counts matrix

pbmc_res

pbmc_res

pbmc_select

pbmc_select

plotDimReduceCluster()

Create a scatterplot based on celda cluster labels.

plotDimReduceGene()

Create a scatterplot for each row of a normalized gene expression matrix where x and y axis are from a data dimensionality reduction tool.

plotDimReduceGrid()

Create a scatterplot given two dimensions from a data dimensionality reduction tool (e.g tSNE)

plotDimReduceState()

Create a scatterplot based off of a matrix containing the celda state probabilities per cell.

plotGridSearchPerplexity(<celda_C>)

Plot perplexity as a function of K from celda_C models

plotGridSearchPerplexity(<celda_CG>)

Plot perplexity as a function of K and L from celda_CG models

plotGridSearchPerplexity(<celda_G>)

Plot perplexity as a function of L from a celda_G model

plotGridSearchPerplexity()

Visualize perplexity of every model in a celda_list, by unique K/L combinations

plotHeatmap()

Renders a heatmap based on a matrix of counts where rows are features and columns are cells.

recodeClusterY()

Re-code gene cluster labels by provided mapping scheme

recodeClusterZ()

Re-code cell cluster labels by provided mapping scheme

runParams()

Get run parameters for a celda run.

sample.cells

sample.cells

selectBestModel()

Select models with the best log likelihood from a 'celda_list' object gererated by celdaGridSearch.

semi_pheatmap()

A function to draw clustered heatmaps.

simulateCells(<celda_C>)

Simulate cells from the cell clustering generative model

simulateCells(<celda_CG>)

Simulate cells from the cell/feature bi-clustering generative model

simulateCells(<celda_G>)

Simulate cells from the feature clustering generative model

simulateCells()

Simulate count data from the celda generative models.

subsetCeldaList()

Select a subset of models from a 'celda_list' object generated by celdaGridSearch.

topRank()

Identify features with the highest influence on clustering.