Function Documentation
Man pages for all functions exported by celda
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available_models
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available models |
calc.perplexity
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calc.perplexity |
calc.perplexity2
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calc.perplexity2 |
calculateLoglikFromVariables.celda_C()
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Calculate the celda_C log likelihood for user-provided cluster assignments |
calculateLoglikFromVariables.celda_CG()
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Calculate log lileklihood for the celda Cell and Gene clustering model, given a set of cell / gene cluster assignments |
calculateLoglikFromVariables.celda_G()
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Calculate the celda_G log likelihood for user-provided cluster assignments |
calculateLoglikFromVariables()
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Calculate a log-likelihood for a user-provided cluster assignment and count matrix, per the desired celda model. |
calculatePerplexity(<celda_C>)
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Calculate the perplexity from a single celda model |
calculatePerplexity(<celda_CG>)
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Calculate the perplexity from a single celda model |
calculatePerplexity(<celda_G>)
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Calculate the perplexity from a single celda model |
calculatePerplexity()
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Calculate the perplexity from a single celda model |
calculatePerplexityWithResampling()
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Calculate and visualize perplexity of all models in a celda_list, with count resampling |
calculateTsne()
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Run the t-SNE algorithm for dimensionality reduction |
cC.decomposeCounts()
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Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
cCG.decomposeCounts()
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Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
celda()
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Deprecation warning for old grid search function |
celdaGridSearch()
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Run the celda Bayesian hierarchical model on a matrix of counts. |
celdaHeatmap(<celda_C>)
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celdaHeatmap for celda Cell clustering function |
celdaHeatmap(<celda_CG>)
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celdaHeatmap for celda Cell and Gene clustering model. |
celdaHeatmap(<celda_G>)
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celdaHeatmap for celda Gene clustering model |
celdaHeatmap()
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Render a stylable heatmap of count data based on celda clustering results. |
celdaProbabilityMap(<celda_C>)
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Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
celdaProbabilityMap(<celda_CG>)
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Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
celdaProbabilityMap()
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Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
celdaTsne(<celda_C>)
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Embeds cells in two dimensions using tSNE based on celda_C results. |
celdaTsne(<celda_CG>)
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Embeds cells in two dimensions using tSNE based on celda_CG results. |
celdaTsne(<celda_G>)
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Embeds cells in two dimensions using tSNE based on celda_CG results. |
celdaTsne()
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Embeds cells in two dimensions using tSNE based on celda_CG results. |
celda_C()
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celda Cell Clustering Model |
celda_CG()
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celda Cell and Gene Clustering Model |
celda_G()
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celda Gene Clustering Model |
cG.decomposeCounts()
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Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
clusterProbability(<celda_C>)
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Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
clusterProbability(<celda_CG>)
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Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
clusterProbability(<celda_G>)
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Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
clusterProbability()
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Get the probability of the cluster assignments generated during a celda run. |
compareCountMatrix()
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Check whether a count matrix was the one used in a given celda run |
completeLogLikelihood()
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Get the complete log likelihood for a given celda model. |
differentialExpression()
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Gene expression markers for cell clusters using MAST |
distinct_colors()
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Generate a distinct palette for coloring different clusters |
eigenMatMultInt()
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Fast matrix multiplication for double x int |
factorizeMatrix(<celda_C>)
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Generate factorized matrices showing each feature's influence on the celda_C model clustering |
factorizeMatrix(<celda_CG>)
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Generate factorized matrices showing each feature's influence on the celda_CG model clustering |
factorizeMatrix(<celda_G>)
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Generate factorized matrices showing each feature's influence on the celda_G model clustering |
factorizeMatrix()
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Generate factorized matrices showing each feature's influence on cell / gene clustering |
fastNormProp()
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Fast normalization for numeric matrix |
fastNormPropLog()
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Fast normalization for numeric matrix |
fastNormPropSqrt()
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Fast normalization for numeric matrix |
featureModuleLookup(<celda_C>)
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Obtain the gene module of a gene of interest |
featureModuleLookup(<celda_CG>)
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Obtain the feature module of a feature of interest |
featureModuleLookup(<celda_G>)
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Obtain the gene module of a gene of interest |
featureModuleLookup()
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Obtain the gene module of a gene of interest |
finalLogLikelihood()
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Get the log likelihood from the final iteration of Gibbs sampling
for a given celda model. |
moduleHeatmap()
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Transcriptional state heatmap |
normalizeCounts()
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Performs normalization, transformation, and/or scaling on a counts matrix |
pbmc_res
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pbmc_res |
pbmc_select
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pbmc_select |
plotDimReduceCluster()
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Create a scatterplot based on celda cluster labels. |
plotDimReduceGene()
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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()
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Create a scatterplot given two dimensions from a data dimensionality reduction tool (e.g tSNE) |
plotDimReduceState()
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Create a scatterplot based off of a matrix containing the celda state probabilities per cell. |
plotGridSearchPerplexity(<celda_C>)
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Plot perplexity as a function of K from celda_C models |
plotGridSearchPerplexity(<celda_CG>)
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Plot perplexity as a function of K and L from celda_CG models |
plotGridSearchPerplexity(<celda_G>)
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Plot perplexity as a function of L from a celda_G model |
plotGridSearchPerplexity()
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Visualize perplexity of every model in a celda_list, by unique K/L combinations |
plotHeatmap()
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Renders a heatmap based on a matrix of counts where rows are features and columns are cells. |
recodeClusterY()
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Re-code gene cluster labels by provided mapping scheme |
recodeClusterZ()
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Re-code cell cluster labels by provided mapping scheme |
runParams()
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Get run parameters for a celda run. |
sample.cells
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sample.cells |
selectBestModel()
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Select models with the best log likelihood from a 'celda_list' object gererated by celdaGridSearch. |
semi_pheatmap()
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A function to draw clustered heatmaps. |
simulateCells(<celda_C>)
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Simulate cells from the cell clustering generative model |
simulateCells(<celda_CG>)
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Simulate cells from the cell/feature bi-clustering generative model |
simulateCells(<celda_G>)
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Simulate cells from the feature clustering generative model |
simulateCells()
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Simulate count data from the celda generative models. |
subsetCeldaList()
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Select a subset of models from a 'celda_list' object generated by celdaGridSearch. |
topRank()
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Identify features with the highest influence on clustering. |