@@ -59,14 +59,23 @@ export(simulateObservedMatrix)

 export(subsetCeldaList)

 export(topRank)

 export(violinPlot)

+exportMethods(bestLogLikelihood)

 exportMethods(celdaHeatmap)

+exportMethods(celdaPerplexity)

 exportMethods(celdaProbabilityMap)

 exportMethods(celdaTsne)

 exportMethods(celdaUmap)

 exportMethods(clusterProbability)

+exportMethods(clusters)

 exportMethods(factorizeMatrix)

 exportMethods(featureModuleLookup)

+exportMethods(logLikelihoodHistory)

+exportMethods(matrixNames)

+exportMethods(params)

 exportMethods(perplexity)

+exportMethods(resList)

+exportMethods(runParams)

+exportMethods(sampleLabel)

 import(RColorBrewer)

 import(data.table)

 import(foreach)

old mode 100755

new mode 100644

@@ -8,12 +8,20 @@ setClass("celdaModel",

 #' @title Get parameter values provided for celda model creation

 #' @description Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @return List. Contains the model-specific parameters for the provided celda model object depending on its class.

 #' @examples

 #' params(celda.CG.mod)

 #' @export

 setGeneric("params",

            function(celda.mod){ standardGeneric("params") })

+#' @title Get parameter values provided for celda model creation

+#' @description Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

+#' @return List. Contains the model-specific parameters for the provided celda model object depending on its class.

+#' @examples

+#' params(celda.CG.mod)

+#' @export

 setMethod("params",

           signature=c(celda.mod="celdaModel"),

           function(celda.mod){  celda.mod@params  })

@@ -21,12 +29,20 @@ setMethod("params",

 #' @title Get feature, cell and sample names from a celda model

 #' @description Retrieves the row, column, and sample names used to generate a celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.

 #' @examples

 #' matrixNames(celda.CG.mod)

 #' @export

 setGeneric("matrixNames",

            function(celda.mod){ standardGeneric("matrixNames") })

+#' @title Get feature, cell and sample names from a celda model

+#' @description Retrieves the row, column, and sample names used to generate a celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

+#' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.

+#' @examples

+#' matrixNames(celda.CG.mod)

+#' @export

 setMethod("matrixNames",

           signature=c(celda.mod="celdaModel"),

           function(celda.mod){  celda.mod@names  })

@@ -34,13 +50,20 @@ setMethod("matrixNames",

 #' @title Get log-likelihood history

 #' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

-#' 

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.

 #' @examples

 #' logLikelihoodHistory(celda.CG.mod)

 #' @export

 setGeneric("logLikelihoodHistory",

            function(celda.mod){ standardGeneric("logLikelihoodHistory") })

+#' @title Get log-likelihood history

+#' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

+#' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.

+#' @examples

+#' logLikelihoodHistory(celda.CG.mod)

+#' @export

 setMethod("logLikelihoodHistory",

            signature=c(celda.mod="celdaModel"),

            function(celda.mod){  celda.mod@completeLogLik  })

@@ -49,11 +72,18 @@ setMethod("logLikelihoodHistory",

 #' @title Get the log-likelihood 

 #' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

 #' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.

+#' @param celda.mod A celda model object of class celda_C, celda_G, or celda_CG.

 #' @examples

 #' bestLogLikelihood(celda.CG.mod)

 #' @export

 setGeneric("bestLogLikelihood",

            function(celda.mod){ standardGeneric("bestLogLikelihood") })

+#' @title Get the log-likelihood 

+#' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

+#' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.

+#' @examples

+#' bestLogLikelihood(celda.CG.mod)

+#' @export

 setMethod("bestLogLikelihood",

            signature=c(celda.mod="celdaModel"),

            function(celda.mod){  celda.mod@finalLogLik  })

@@ -61,12 +91,20 @@ setMethod("bestLogLikelihood",

 #' @title Get clustering outcomes from a celda model

 #' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)

 #' @examples

 #' clusters(celda.CG.mod)

 #' @export

 setGeneric("clusters",

            function(celda.mod){ standardGeneric("clusters")})

+#' @title Get clustering outcomes from a celda model

+#' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

+#' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)

+#' @examples

+#' clusters(celda.CG.mod)

+#' @export

 setMethod("clusters", signature=c(celda.mod="celdaModel"),

           function(celda.mod){

             return(celda.mod@clusters)

@@ -80,12 +118,20 @@ setClass("celda_C",

 #' @title Get sample labels from a celda model

 #' @description Returns the sample labels for the count matrix provided for generation of a given celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.

 #' @examples

 #' sampleLabel(celda.CG.mod)

 #' @export

 setGeneric("sampleLabel",

            function(celda.mod){ standardGeneric("sampleLabel") })

+#' @title Get sample labels from a celda model

+#' @description Returns the sample labels for the count matrix provided for generation of a given celda model.

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

+#' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.

+#' @examples

+#' sampleLabel(celda.CG.mod)

+#' @export

 setMethod("sampleLabel",

            signature=c(celda.mod="celdaModel"),

            function(celda.mod){  celda.mod@sample.label  })

@@ -106,12 +152,20 @@ setClass("celdaList",

 #' @title Get run parameters provided to `celdaGridSearch()`

 #' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.

+#' @param celda.mod An object of class celdaList.

 #' @return Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.

 #' @examples

 #' runParams(celda.CG.grid.search.res)

 #' @export

 setGeneric("runParams",

            function(celda.mod){ standardGeneric("runParams") })

+#' @title Get run parameters provided to `celdaGridSearch()`

+#' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.

+#' @param celda.mod An object of class celdaList.

+#' @return Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.

+#' @examples

+#' runParams(celda.CG.grid.search.res)

+#' @export

 setMethod("runParams",

            signature=c(celda.mod="celdaList"),

            function(celda.mod){  celda.mod@run.params  })

@@ -119,12 +173,20 @@ setMethod("runParams",

 #' @title Get final celda models from a celdaList

 #' @description Returns all models generated during a `celdaGridSearch()` run.

+#' @param celda.mod An object of class celdaList.

 #' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

 #' @examples

 #' celda.CG.grid.models = resList(celda.CG.grid.search.res)

 #' @export

 setGeneric("resList",

            function(celda.mod){ standardGeneric("resList") })

+#' @title Get final celda models from a celdaList

+#' @description Returns all models generated during a `celdaGridSearch()` run.

+#' @param celda.mod An object of class celdaList.

+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

+#' @examples

+#' celda.CG.grid.models = resList(celda.CG.grid.search.res)

+#' @export

 setMethod("resList",

            signature=c(celda.mod="celdaList"),

            function(celda.mod){  celda.mod@res.list  })

@@ -132,12 +194,19 @@ setMethod("resList",

 #' @title Get perplexity for every model in a celdaList

 #' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().`

+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG".

 #' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

 #' @examples

 #' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)

 #' @export

 setGeneric("celdaPerplexity",

            function(celda.mod){ standardGeneric("celdaPerplexity") })

+#' @title Get perplexity for every model in a celdaList

+#' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().`

+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

+#' @examples

+#' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)

+#' @export

 setMethod("celdaPerplexity",

            signature=c(celda.mod="celdaList"),

            function(celda.mod){  celda.mod@perplexity  })

@@ -146,6 +215,8 @@ setMethod("celdaPerplexity",

 #' @title Append two celdaList objects

 #' @description Returns a single celdaList representing the combination of two provided celdaList objects.

 #' @return A celdaList object. This object contains all resList entries and runParam records from both lists.

+#' @param list1 A celda_list object

+#' @param list2 A celda_list object to be joined with list_1

 #' @examples

 #' appended.list = appendCeldaList(celda.CG.grid.search.res, celda.CG.grid.search.res)

 #' @export

@@ -172,7 +243,8 @@ appendCeldaList = function(list1, list2) {

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

 #'

 #' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`. 

-#' @param celda.mod Celda object of class "celda_C", "celda_G", or "celda_CG".

+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG".

+#' @param feature.ix Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL.

 #' @param ... Additional parameters.

 #' @examples 

 #' celdaHeatmap(celda.CG.sim$counts, celda.CG.mod)

@@ -212,13 +284,14 @@ logLikelihood = function(counts, model, ...) {

 #' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.

 #' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @param log Logical. If FALSE, then the normalized conditional probabilities will be returned. If TRUE, then the unnormalized log probabilities will be returned. Default FALSE.  

+#' @param ... Additional parameters.

 #' @examples

 #' cluster.prob = clusterProbability(celda.CG.sim$counts, celda.CG.mod)

 #' @return A numeric vector of the cluster assignment probabilties

 #' @export

 setGeneric("clusterProbability", 

            signature="celda.mod",

-           function(counts, celda.mod, log=FALSE, modules=NULL, ...) {

+           function(counts, celda.mod, log=FALSE, ...) {

              standardGeneric("clusterProbability")

            })

@@ -229,7 +302,7 @@ setGeneric("clusterProbability",

 #' cluster assignments fit the data being clustered.

 #' 

 #' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.

-#' @param celda.mod Celda object of class "celda_C", "celda_G" or "celda_CG".

+#' @param celda.mod Celda model. Options available in `celda::available.models`.

 #' @param new.counts A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL.

 #' @return Numeric. The perplexity for the provided count data and model.

 #' @examples

@@ -308,7 +381,6 @@ setGeneric("celdaProbabilityMap",

 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

 #' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.

 #' @param ... Additional parameters.

-#' @param ... Additional parameters.

 #' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.

 #' @examples 

 #' tsne.res = celdaTsne(celda.CG.sim$counts, celda.CG.mod)

@@ -331,10 +403,7 @@ setGeneric("celdaTsne",

 #' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000.

 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 

 #' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.

-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.

-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.

-#' @param ... Additional parameters.

+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm.

 #' @param ... Additional parameters.

 #' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.

 #' @examples 

@@ -421,6 +421,7 @@ cC.calcLL = function(m.CP.by.S, n.G.by.CP, s, z, K, nS, nG, alpha, beta) {

 #' @description Calculates the log likelihood for user-provided cell population clusters using the `celda_C()` model.

 #' 

 #' @param counts Integer matrix. Rows represent features and columns represent cells. 

+#' @param model An object of class celda_C.

 #' @param sample.label Vector or factor. Denotes the sample label for each cell (column) in the count matrix.

 #' @param z Numeric vector. Denotes cell population labels. 

 #' @param K Integer. Number of cell populations. 

@@ -605,6 +606,7 @@ setMethod("celdaHeatmap",

 #' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000.

 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 

 #' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.

+#' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.

 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.

 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

 #' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.

@@ -641,10 +643,8 @@ setMethod("celdaTsne",

 #' @param celda.mod Celda object of class `celda_C`. 

 #' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000.

 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 

-#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.

-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.

-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.

+#' @param modules Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.

+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm.

 #' @param ... Additional parameters.

 #' @seealso `celda_C()` for clustering cells and `celdaHeatmap()` for displaying expression

 #' @examples

@@ -731,6 +731,7 @@ setMethod("celdaHeatmap",

 #' @param celda.mod Celda object of class `celda_CG`. 

 #' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000.

 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 

+#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.

 #' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.

 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.

 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

@@ -589,6 +589,8 @@ setMethod("celdaHeatmap",

 #' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.

 #' @param celda.mod Celda object of class `celda_G`.  

 #' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(conts) > max.cells. Larger numbers of cells requires more memory. Default 10000.

+#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 

+#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.

 #' @param modules Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL.

 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.

 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.

@@ -6,6 +6,11 @@

 \usage{

 appendCeldaList(list1, list2)

 }

+\arguments{

+\item{list1}{A celda_list object}

+

+\item{list2}{A celda_list object to be joined with list_1}

+}

 \value{

 A celdaList object. This object contains all resList entries and runParam records from both lists.

 }

new file mode 100644

@@ -0,0 +1,18 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{bestLogLikelihood,celdaModel-method}

+\alias{bestLogLikelihood,celdaModel-method}

+\title{Get the log-likelihood}

+\usage{

+\S4method{bestLogLikelihood}{celdaModel}(celda.mod)

+}

+\value{

+Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.

+}

+\description{

+Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

+}

+\examples{

+bestLogLikelihood(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 bestLogLikelihood(celda.mod)

 }

+\arguments{

+\item{celda.mod}{A celda model object of class celda_C, celda_G, or celda_CG.}

+}

 \value{

 Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.

 }

@@ -6,7 +6,8 @@

 \usage{

 celdaGridSearch(counts, model, params.test, params.fixed = NULL,

   max.iter = 200, nchains = 3, cores = 1, best.only = TRUE,

-  seed = 12345, verbose = TRUE, logfile.prefix = "Celda")

+  seed = 12345, perplexity = TRUE, verbose = TRUE,

+  logfile.prefix = "Celda")

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -27,6 +28,8 @@ celdaGridSearch(counts, model, params.test, params.fixed = NULL,

 \item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}

+\item{perplexity}{Logical. Whether to calculate perplexity for each model. If FALSE, then perplexity can be calculated later with `resamplePerplexity()`. Default TRUE.}

+

 \item{verbose}{Logical. Whether to print log messages during celda chain execution. Default TRUE.}

 \item{logfile.prefix}{Character. Prefix for log files from worker threads and main process. Default "Celda".}

@@ -9,7 +9,9 @@ celdaHeatmap(counts, celda.mod, feature.ix, ...)

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

-\item{celda.mod}{Celda object of class "celda_C", "celda_G", or "celda_CG".}

+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".}

+

+\item{feature.ix}{Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL.}

 \item{...}{Additional parameters.}

 }

new file mode 100644

@@ -0,0 +1,18 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{celdaPerplexity,celdaList-method}

+\alias{celdaPerplexity,celdaList-method}

+\title{Get perplexity for every model in a celdaList}

+\usage{

+\S4method{celdaPerplexity}{celdaList}(celda.mod)

+}

+\value{

+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

+}

+\description{

+Returns perplexity for each model in a celdaList as calculated by `perplexity().`

+}

+\examples{

+celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)

+}

@@ -6,6 +6,9 @@

 \usage{

 celdaPerplexity(celda.mod)

 }

+\arguments{

+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".}

+}

 \value{

 List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

 }

@@ -20,6 +20,8 @@

 \item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.}

+\item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}

+

 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}

 \item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}

@@ -18,6 +18,8 @@

 \item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}

+\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.}

+

 \item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}

 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}

@@ -16,6 +16,10 @@

 \item{max.cells}{Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(conts) > max.cells. Larger numbers of cells requires more memory. Default 10000.}

+\item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}

+

+\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.}

+

 \item{modules}{Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL.}

 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}

@@ -25,8 +25,6 @@ celdaTsne(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,

 \item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}

-\item{...}{Additional parameters.}

-

 \item{...}{Additional parameters.}

 }

 \value{

@@ -18,13 +18,9 @@

 \item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}

-\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.}

+\item{modules}{Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.}

-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}

-

-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}

-

-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}

+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.}

 \item{...}{Additional parameters.}

 }

@@ -18,13 +18,7 @@ celdaUmap(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,

 \item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}

-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}

-

-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}

-

-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}

-

-\item{...}{Additional parameters.}

+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.}

 \item{...}{Additional parameters.}

 }

@@ -6,9 +6,9 @@

 \usage{

 celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10,

   max.iter = 200, split.on.iter = 10, split.on.last = TRUE,

-  seed = 12345, nchains = 3, y.initialize = c("split", "random"),

-  count.checksum = NULL, y.init = NULL, logfile = NULL,

-  verbose = TRUE)

+  seed = 12345, nchains = 3, y.initialize = c("split", "random",

+  "predefined"), count.checksum = NULL, y.init = NULL,

+  logfile = NULL, verbose = TRUE)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -6,7 +6,7 @@

 \title{Conditional probabilities for cells in subpopulations from a Celda_C model}

 \usage{

 \S4method{clusterProbability}{celda_C}(counts, celda.mod, log = FALSE,

-  modules = NULL, ...)

+  ...)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

@@ -6,7 +6,7 @@

 \title{Conditional probabilities for cells and features from a Celda_CG model}

 \usage{

 \S4method{clusterProbability}{celda_CG}(counts, celda.mod, log = FALSE,

-  modules = NULL, ...)

+  ...)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

@@ -6,7 +6,7 @@

 \title{Conditional probabilities for features in modules from a Celda_G model}

 \usage{

 \S4method{clusterProbability}{celda_G}(counts, celda.mod, log = FALSE,

-  modules = NULL, ...)

+  ...)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

@@ -4,7 +4,7 @@

 \alias{clusterProbability}

 \title{Get the probability of the cluster assignments generated during a celda run.}

 \usage{

-clusterProbability(counts, celda.mod, log = FALSE, modules = NULL, ...)

+clusterProbability(counts, celda.mod, log = FALSE, ...)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

@@ -12,6 +12,8 @@ clusterProbability(counts, celda.mod, log = FALSE, modules = NULL, ...)

 \item{celda.mod}{Celda model. Options available in `celda::available.models`.}

 \item{log}{Logical. If FALSE, then the normalized conditional probabilities will be returned. If TRUE, then the unnormalized log probabilities will be returned. Default FALSE.}

+

+\item{...}{Additional parameters.}

 }

 \value{

 A numeric vector of the cluster assignment probabilties

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{clusters,celdaModel-method}

+\alias{clusters,celdaModel-method}

+\title{Get clustering outcomes from a celda model}

+\usage{

+\S4method{clusters}{celdaModel}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

+\value{

+List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)

+}

+\description{

+Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.

+}

+\examples{

+clusters(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 clusters(celda.mod)

 }

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

 \value{

 List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)

 }

@@ -9,6 +9,8 @@ logLikelihood.celda_C(counts, model, sample.label, z, K, alpha, beta)

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

+\item{model}{An object of class celda_C.}

+

 \item{sample.label}{Vector or factor. Denotes the sample label for each cell (column) in the count matrix.}

 \item{z}{Numeric vector. Denotes cell population labels.}

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{logLikelihoodHistory,celdaModel-method}

+\alias{logLikelihoodHistory,celdaModel-method}

+\title{Get log-likelihood history}

+\usage{

+\S4method{logLikelihoodHistory}{celdaModel}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

+\value{

+Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.

+}

+\description{

+Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.

+}

+\examples{

+logLikelihoodHistory(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 logLikelihoodHistory(celda.mod)

 }

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

 \value{

 Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{matrixNames,celdaModel-method}

+\alias{matrixNames,celdaModel-method}

+\title{Get feature, cell and sample names from a celda model}

+\usage{

+\S4method{matrixNames}{celdaModel}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

+\value{

+List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.

+}

+\description{

+Retrieves the row, column, and sample names used to generate a celda model.

+}

+\examples{

+matrixNames(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 matrixNames(celda.mod)

 }

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

 \value{

 List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{params,celdaModel-method}

+\alias{params,celdaModel-method}

+\title{Get parameter values provided for celda model creation}

+\usage{

+\S4method{params}{celdaModel}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

+\value{

+List. Contains the model-specific parameters for the provided celda model object depending on its class.

+}

+\description{

+Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model.

+}

+\examples{

+params(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 params(celda.mod)

 }

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

 \value{

 List. Contains the model-specific parameters for the provided celda model object depending on its class.

 }

@@ -9,7 +9,7 @@ perplexity(counts, celda.mod, new.counts = NULL)

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

-\item{celda.mod}{Celda object of class "celda_C", "celda_G" or "celda_CG".}

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

 \item{new.counts}{A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL.}

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{resList,celdaList-method}

+\alias{resList,celdaList-method}

+\title{Get final celda models from a celdaList}

+\usage{

+\S4method{resList}{celdaList}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{An object of class celdaList.}

+}

+\value{

+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

+}

+\description{

+Returns all models generated during a `celdaGridSearch()` run.

+}

+\examples{

+celda.CG.grid.models = resList(celda.CG.grid.search.res)

+}

@@ -6,6 +6,9 @@

 \usage{

 resList(celda.mod)

 }

+\arguments{

+\item{celda.mod}{An object of class celdaList.}

+}

 \value{

 List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{runParams,celdaList-method}

+\alias{runParams,celdaList-method}

+\title{Get run parameters provided to `celdaGridSearch()`}

+\usage{

+\S4method{runParams}{celdaList}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{An object of class celdaList.}

+}

+\value{

+Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.

+}

+\description{

+Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.

+}

+\examples{

+runParams(celda.CG.grid.search.res)

+}

@@ -6,6 +6,9 @@

 \usage{

 runParams(celda.mod)

 }

+\arguments{

+\item{celda.mod}{An object of class celdaList.}

+}

 \value{

 Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\docType{methods}

+\name{sampleLabel,celdaModel-method}

+\alias{sampleLabel,celdaModel-method}

+\title{Get sample labels from a celda model}

+\usage{

+\S4method{sampleLabel}{celdaModel}(celda.mod)

+}

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

+\value{

+Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.

+}

+\description{

+Returns the sample labels for the count matrix provided for generation of a given celda model.

+}

+\examples{

+sampleLabel(celda.CG.mod)

+}

@@ -6,6 +6,9 @@

 \usage{

 sampleLabel(celda.mod)

 }

+\arguments{

+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}

+}

 \value{

 Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.

 }