Browse code

Adding missing documentation for Bioconductor build

Sean Corbett authored on 19/03/2019 00:45:46
Showing 40 changed files

... ...
@@ -59,14 +59,23 @@ export(simulateObservedMatrix)
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 export(subsetCeldaList)
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 export(topRank)
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 export(violinPlot)
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+exportMethods(bestLogLikelihood)
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 exportMethods(celdaHeatmap)
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+exportMethods(celdaPerplexity)
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 exportMethods(celdaProbabilityMap)
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 exportMethods(celdaTsne)
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 exportMethods(celdaUmap)
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 exportMethods(clusterProbability)
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+exportMethods(clusters)
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 exportMethods(factorizeMatrix)
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 exportMethods(featureModuleLookup)
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+exportMethods(logLikelihoodHistory)
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+exportMethods(matrixNames)
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+exportMethods(params)
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 exportMethods(perplexity)
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+exportMethods(resList)
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+exportMethods(runParams)
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+exportMethods(sampleLabel)
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 import(RColorBrewer)
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 import(data.table)
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 import(foreach)
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old mode 100755
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new mode 100644
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@@ -8,12 +8,20 @@ setClass("celdaModel",
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 #' @title Get parameter values provided for celda model creation
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 #' @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.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @return List. Contains the model-specific parameters for the provided celda model object depending on its class.
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 #' @examples
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 #' params(celda.CG.mod)
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 #' @export
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 setGeneric("params",
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            function(celda.mod){ standardGeneric("params") })
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+#' @title Get parameter values provided for celda model creation
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+#' @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.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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+#' @return List. Contains the model-specific parameters for the provided celda model object depending on its class.
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+#' @examples
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+#' params(celda.CG.mod)
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+#' @export
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 setMethod("params",
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           signature=c(celda.mod="celdaModel"),
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           function(celda.mod){  celda.mod@params  })
... ...
@@ -21,12 +29,20 @@ setMethod("params",
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 #' @title Get feature, cell and sample names from a celda model
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 #' @description Retrieves the row, column, and sample names used to generate a celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.
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 #' @examples
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 #' matrixNames(celda.CG.mod)
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 #' @export
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 setGeneric("matrixNames",
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            function(celda.mod){ standardGeneric("matrixNames") })
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+#' @title Get feature, cell and sample names from a celda model
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+#' @description Retrieves the row, column, and sample names used to generate a celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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+#' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.
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+#' @examples
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+#' matrixNames(celda.CG.mod)
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+#' @export
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 setMethod("matrixNames",
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           signature=c(celda.mod="celdaModel"),
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           function(celda.mod){  celda.mod@names  })
... ...
@@ -34,13 +50,20 @@ setMethod("matrixNames",
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 #' @title Get log-likelihood history
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 #' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
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-#' 
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.
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 #' @examples
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 #' logLikelihoodHistory(celda.CG.mod)
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 #' @export
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 setGeneric("logLikelihoodHistory",
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            function(celda.mod){ standardGeneric("logLikelihoodHistory") })
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+#' @title Get log-likelihood history
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+#' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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+#' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.
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+#' @examples
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+#' logLikelihoodHistory(celda.CG.mod)
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+#' @export
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 setMethod("logLikelihoodHistory",
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            signature=c(celda.mod="celdaModel"),
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            function(celda.mod){  celda.mod@completeLogLik  })
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@@ -49,11 +72,18 @@ setMethod("logLikelihoodHistory",
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 #' @title Get the log-likelihood 
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 #' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
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 #' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.
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+#' @param celda.mod A celda model object of class celda_C, celda_G, or celda_CG.
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 #' @examples
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 #' bestLogLikelihood(celda.CG.mod)
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 #' @export
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 setGeneric("bestLogLikelihood",
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            function(celda.mod){ standardGeneric("bestLogLikelihood") })
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+#' @title Get the log-likelihood 
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+#' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
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+#' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.
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+#' @examples
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+#' bestLogLikelihood(celda.CG.mod)
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+#' @export
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 setMethod("bestLogLikelihood",
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            signature=c(celda.mod="celdaModel"),
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            function(celda.mod){  celda.mod@finalLogLik  })
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@@ -61,12 +91,20 @@ setMethod("bestLogLikelihood",
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 #' @title Get clustering outcomes from a celda model
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 #' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)
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 #' @examples
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 #' clusters(celda.CG.mod)
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 #' @export
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 setGeneric("clusters",
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            function(celda.mod){ standardGeneric("clusters")})
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+#' @title Get clustering outcomes from a celda model
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+#' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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+#' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)
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+#' @examples
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+#' clusters(celda.CG.mod)
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+#' @export
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 setMethod("clusters", signature=c(celda.mod="celdaModel"),
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           function(celda.mod){
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             return(celda.mod@clusters)
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@@ -80,12 +118,20 @@ setClass("celda_C",
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 #' @title Get sample labels from a celda model
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 #' @description Returns the sample labels for the count matrix provided for generation of a given celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.
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 #' @examples
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 #' sampleLabel(celda.CG.mod)
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 #' @export
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 setGeneric("sampleLabel",
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            function(celda.mod){ standardGeneric("sampleLabel") })
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+#' @title Get sample labels from a celda model
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+#' @description Returns the sample labels for the count matrix provided for generation of a given celda model.
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+#' @param celda.mod Celda model. Options available in `celda::available.models`.
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+#' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.
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+#' @examples
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+#' sampleLabel(celda.CG.mod)
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+#' @export
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 setMethod("sampleLabel",
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            signature=c(celda.mod="celdaModel"),
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            function(celda.mod){  celda.mod@sample.label  })
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@@ -106,12 +152,20 @@ setClass("celdaList",
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 #' @title Get run parameters provided to `celdaGridSearch()`
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 #' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.
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+#' @param celda.mod An object of class celdaList.
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 #' @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.
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 #' @examples
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 #' runParams(celda.CG.grid.search.res)
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 #' @export
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 setGeneric("runParams",
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            function(celda.mod){ standardGeneric("runParams") })
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+#' @title Get run parameters provided to `celdaGridSearch()`
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+#' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.
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+#' @param celda.mod An object of class celdaList.
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+#' @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.
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+#' @examples
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+#' runParams(celda.CG.grid.search.res)
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+#' @export
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 setMethod("runParams",
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            signature=c(celda.mod="celdaList"),
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            function(celda.mod){  celda.mod@run.params  })
... ...
@@ -119,12 +173,20 @@ setMethod("runParams",
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 #' @title Get final celda models from a celdaList
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 #' @description Returns all models generated during a `celdaGridSearch()` run.
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+#' @param celda.mod An object of class celdaList.
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 #' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
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 #' @examples
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 #' celda.CG.grid.models = resList(celda.CG.grid.search.res)
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 #' @export
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 setGeneric("resList",
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            function(celda.mod){ standardGeneric("resList") })
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+#' @title Get final celda models from a celdaList
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+#' @description Returns all models generated during a `celdaGridSearch()` run.
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+#' @param celda.mod An object of class celdaList.
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+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
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+#' @examples
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+#' celda.CG.grid.models = resList(celda.CG.grid.search.res)
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+#' @export
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 setMethod("resList",
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            signature=c(celda.mod="celdaList"),
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            function(celda.mod){  celda.mod@res.list  })
... ...
@@ -132,12 +194,19 @@ setMethod("resList",
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 #' @title Get perplexity for every model in a celdaList
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 #' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().`
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+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG".
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 #' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
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 #' @examples
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 #' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)
138 201
 #' @export
139 202
 setGeneric("celdaPerplexity",
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            function(celda.mod){ standardGeneric("celdaPerplexity") })
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+#' @title Get perplexity for every model in a celdaList
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+#' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().`
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+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
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+#' @examples
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+#' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)
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+#' @export
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 setMethod("celdaPerplexity",
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            signature=c(celda.mod="celdaList"),
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            function(celda.mod){  celda.mod@perplexity  })
... ...
@@ -146,6 +215,8 @@ setMethod("celdaPerplexity",
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 #' @title Append two celdaList objects
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 #' @description Returns a single celdaList representing the combination of two provided celdaList objects.
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 #' @return A celdaList object. This object contains all resList entries and runParam records from both lists.
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+#' @param list1 A celda_list object
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+#' @param list2 A celda_list object to be joined with list_1
149 220
 #' @examples
150 221
 #' appended.list = appendCeldaList(celda.CG.grid.search.res, celda.CG.grid.search.res)
151 222
 #' @export
... ...
@@ -172,7 +243,8 @@ appendCeldaList = function(list1, list2) {
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 #' Render a stylable heatmap of count data based on celda clustering results.
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 #'
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 #' @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`. 
175
-#' @param celda.mod Celda object of class "celda_C", "celda_G", or "celda_CG".
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+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG".
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+#' @param feature.ix Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL.
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 #' @param ... Additional parameters.
177 249
 #' @examples 
178 250
 #' celdaHeatmap(celda.CG.sim$counts, celda.CG.mod)
... ...
@@ -212,13 +284,14 @@ logLikelihood = function(counts, model, ...) {
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 #' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.
213 285
 #' @param celda.mod Celda model. Options available in `celda::available.models`.
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 #' @param log Logical. If FALSE, then the normalized conditional probabilities will be returned. If TRUE, then the unnormalized log probabilities will be returned. Default FALSE.  
287
+#' @param ... Additional parameters.
215 288
 #' @examples
216 289
 #' cluster.prob = clusterProbability(celda.CG.sim$counts, celda.CG.mod)
217 290
 #' @return A numeric vector of the cluster assignment probabilties
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 #' @export
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 setGeneric("clusterProbability", 
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            signature="celda.mod",
221
-           function(counts, celda.mod, log=FALSE, modules=NULL, ...) {
294
+           function(counts, celda.mod, log=FALSE, ...) {
222 295
              standardGeneric("clusterProbability")
223 296
            })
224 297
 
... ...
@@ -229,7 +302,7 @@ setGeneric("clusterProbability",
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 #' cluster assignments fit the data being clustered.
230 303
 #' 
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 #' @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`.
232
-#' @param celda.mod Celda object of class "celda_C", "celda_G" or "celda_CG".
305
+#' @param celda.mod Celda model. Options available in `celda::available.models`.
233 306
 #' @param new.counts A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL.
234 307
 #' @return Numeric. The perplexity for the provided count data and model.
235 308
 #' @examples
... ...
@@ -308,7 +381,6 @@ setGeneric("celdaProbabilityMap",
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 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
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 #' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.
310 383
 #' @param ... Additional parameters.
311
-#' @param ... Additional parameters.
312 384
 #' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.
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 #' @examples 
314 386
 #' tsne.res = celdaTsne(celda.CG.sim$counts, celda.CG.mod)
... ...
@@ -331,10 +403,7 @@ setGeneric("celdaTsne",
331 403
 #' @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.
332 404
 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 
333 405
 #' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.
334
-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.
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-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
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-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.
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-#' @param ... Additional parameters.
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+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm.
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 #' @param ... Additional parameters.
339 408
 #' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.
340 409
 #' @examples 
... ...
@@ -421,6 +421,7 @@ cC.calcLL = function(m.CP.by.S, n.G.by.CP, s, z, K, nS, nG, alpha, beta) {
421 421
 #' @description Calculates the log likelihood for user-provided cell population clusters using the `celda_C()` model.
422 422
 #' 
423 423
 #' @param counts Integer matrix. Rows represent features and columns represent cells. 
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+#' @param model An object of class celda_C.
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 #' @param sample.label Vector or factor. Denotes the sample label for each cell (column) in the count matrix.
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 #' @param z Numeric vector. Denotes cell population labels. 
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 #' @param K Integer. Number of cell populations. 
... ...
@@ -605,6 +606,7 @@ setMethod("celdaHeatmap",
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 #' @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.
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 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 
607 608
 #' @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.
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+#' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.
608 610
 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.
609 611
 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
610 612
 #' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.
... ...
@@ -641,10 +643,8 @@ setMethod("celdaTsne",
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 #' @param celda.mod Celda object of class `celda_C`. 
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 #' @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.
643 645
 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 
644
-#' @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.
645
-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.
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-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
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-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.
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+#' @param modules Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.
647
+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm.
648 648
 #' @param ... Additional parameters.
649 649
 #' @seealso `celda_C()` for clustering cells and `celdaHeatmap()` for displaying expression
650 650
 #' @examples
... ...
@@ -731,6 +731,7 @@ setMethod("celdaHeatmap",
731 731
 #' @param celda.mod Celda object of class `celda_CG`. 
732 732
 #' @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.
733 733
 #' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 
734
+#' @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.
734 735
 #' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.
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 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.
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 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
... ...
@@ -589,6 +589,8 @@ setMethod("celdaHeatmap",
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 #' @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`.
590 590
 #' @param celda.mod Celda object of class `celda_G`.  
591 591
 #' @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.
592
+#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. 
593
+#' @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.
592 594
 #' @param modules Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL.
593 595
 #' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20.
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 #' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500.
... ...
@@ -6,6 +6,11 @@
6 6
 \usage{
7 7
 appendCeldaList(list1, list2)
8 8
 }
9
+\arguments{
10
+\item{list1}{A celda_list object}
11
+
12
+\item{list2}{A celda_list object to be joined with list_1}
13
+}
9 14
 \value{
10 15
 A celdaList object. This object contains all resList entries and runParam records from both lists.
11 16
 }
12 17
new file mode 100644
... ...
@@ -0,0 +1,18 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{bestLogLikelihood,celdaModel-method}
5
+\alias{bestLogLikelihood,celdaModel-method}
6
+\title{Get the log-likelihood}
7
+\usage{
8
+\S4method{bestLogLikelihood}{celdaModel}(celda.mod)
9
+}
10
+\value{
11
+Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.
12
+}
13
+\description{
14
+Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
15
+}
16
+\examples{
17
+bestLogLikelihood(celda.CG.mod)
18
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 bestLogLikelihood(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{A celda model object of class celda_C, celda_G, or celda_CG.}
11
+}
9 12
 \value{
10 13
 Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model.
11 14
 }
... ...
@@ -6,7 +6,8 @@
6 6
 \usage{
7 7
 celdaGridSearch(counts, model, params.test, params.fixed = NULL,
8 8
   max.iter = 200, nchains = 3, cores = 1, best.only = TRUE,
9
-  seed = 12345, verbose = TRUE, logfile.prefix = "Celda")
9
+  seed = 12345, perplexity = TRUE, verbose = TRUE,
10
+  logfile.prefix = "Celda")
10 11
 }
11 12
 \arguments{
12 13
 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}
... ...
@@ -27,6 +28,8 @@ celdaGridSearch(counts, model, params.test, params.fixed = NULL,
27 28
 
28 29
 \item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}
29 30
 
31
+\item{perplexity}{Logical. Whether to calculate perplexity for each model. If FALSE, then perplexity can be calculated later with `resamplePerplexity()`. Default TRUE.}
32
+
30 33
 \item{verbose}{Logical. Whether to print log messages during celda chain execution. Default TRUE.}
31 34
 
32 35
 \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, ...)
9 9
 \arguments{
10 10
 \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`.}
11 11
 
12
-\item{celda.mod}{Celda object of class "celda_C", "celda_G", or "celda_CG".}
12
+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".}
13
+
14
+\item{feature.ix}{Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL.}
13 15
 
14 16
 \item{...}{Additional parameters.}
15 17
 }
16 18
new file mode 100644
... ...
@@ -0,0 +1,18 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{celdaPerplexity,celdaList-method}
5
+\alias{celdaPerplexity,celdaList-method}
6
+\title{Get perplexity for every model in a celdaList}
7
+\usage{
8
+\S4method{celdaPerplexity}{celdaList}(celda.mod)
9
+}
10
+\value{
11
+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
12
+}
13
+\description{
14
+Returns perplexity for each model in a celdaList as calculated by `perplexity().`
15
+}
16
+\examples{
17
+celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res)
18
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 celdaPerplexity(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".}
11
+}
9 12
 \value{
10 13
 List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
11 14
 }
... ...
@@ -20,6 +20,8 @@
20 20
 
21 21
 \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.}
22 22
 
23
+\item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}
24
+
23 25
 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}
24 26
 
25 27
 \item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}
... ...
@@ -18,6 +18,8 @@
18 18
 
19 19
 \item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}
20 20
 
21
+\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.}
22
+
21 23
 \item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}
22 24
 
23 25
 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}
... ...
@@ -16,6 +16,10 @@
16 16
 
17 17
 \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.}
18 18
 
19
+\item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}
20
+
21
+\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.}
22
+
19 23
 \item{modules}{Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL.}
20 24
 
21 25
 \item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}
... ...
@@ -25,8 +25,6 @@ celdaTsne(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,
25 25
 
26 26
 \item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}
27 27
 
28
-\item{...}{Additional parameters.}
29
-
30 28
 \item{...}{Additional parameters.}
31 29
 }
32 30
 \value{
... ...
@@ -18,13 +18,9 @@
18 18
 
19 19
 \item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.}
20 20
 
21
-\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.}
21
+\item{modules}{Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.}
22 22
 
23
-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}
24
-
25
-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}
26
-
27
-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}
23
+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.}
28 24
 
29 25
 \item{...}{Additional parameters.}
30 26
 }
... ...
@@ -18,13 +18,7 @@ celdaUmap(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,
18 18
 
19 19
 \item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.}
20 20
 
21
-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.}
22
-
23
-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.}
24
-
25
-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.}
26
-
27
-\item{...}{Additional parameters.}
21
+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.}
28 22
 
29 23
 \item{...}{Additional parameters.}
30 24
 }
... ...
@@ -6,9 +6,9 @@
6 6
 \usage{
7 7
 celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10,
8 8
   max.iter = 200, split.on.iter = 10, split.on.last = TRUE,
9
-  seed = 12345, nchains = 3, y.initialize = c("split", "random"),
10
-  count.checksum = NULL, y.init = NULL, logfile = NULL,
11
-  verbose = TRUE)
9
+  seed = 12345, nchains = 3, y.initialize = c("split", "random",
10
+  "predefined"), count.checksum = NULL, y.init = NULL,
11
+  logfile = NULL, verbose = TRUE)
12 12
 }
13 13
 \arguments{
14 14
 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}
... ...
@@ -6,7 +6,7 @@
6 6
 \title{Conditional probabilities for cells in subpopulations from a Celda_C model}
7 7
 \usage{
8 8
 \S4method{clusterProbability}{celda_C}(counts, celda.mod, log = FALSE,
9
-  modules = NULL, ...)
9
+  ...)
10 10
 }
11 11
 \arguments{
12 12
 \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 @@
6 6
 \title{Conditional probabilities for cells and features from a Celda_CG model}
7 7
 \usage{
8 8
 \S4method{clusterProbability}{celda_CG}(counts, celda.mod, log = FALSE,
9
-  modules = NULL, ...)
9
+  ...)
10 10
 }
11 11
 \arguments{
12 12
 \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 @@
6 6
 \title{Conditional probabilities for features in modules from a Celda_G model}
7 7
 \usage{
8 8
 \S4method{clusterProbability}{celda_G}(counts, celda.mod, log = FALSE,
9
-  modules = NULL, ...)
9
+  ...)
10 10
 }
11 11
 \arguments{
12 12
 \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 @@
4 4
 \alias{clusterProbability}
5 5
 \title{Get the probability of the cluster assignments generated during a celda run.}
6 6
 \usage{
7
-clusterProbability(counts, celda.mod, log = FALSE, modules = NULL, ...)
7
+clusterProbability(counts, celda.mod, log = FALSE, ...)
8 8
 }
9 9
 \arguments{
10 10
 \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, ...)
12 12
 \item{celda.mod}{Celda model. Options available in `celda::available.models`.}
13 13
 
14 14
 \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.}
15
+
16
+\item{...}{Additional parameters.}
15 17
 }
16 18
 \value{
17 19
 A numeric vector of the cluster assignment probabilties
18 20
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{clusters,celdaModel-method}
5
+\alias{clusters,celdaModel-method}
6
+\title{Get clustering outcomes from a celda model}
7
+\usage{
8
+\S4method{clusters}{celdaModel}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
12
+}
13
+\value{
14
+List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)
15
+}
16
+\description{
17
+Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model.
18
+}
19
+\examples{
20
+clusters(celda.CG.mod)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 clusters(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
11
+}
9 12
 \value{
10 13
 List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models)
11 14
 }
... ...
@@ -9,6 +9,8 @@ logLikelihood.celda_C(counts, model, sample.label, z, K, alpha, beta)
9 9
 \arguments{
10 10
 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}
11 11
 
12
+\item{model}{An object of class celda_C.}
13
+
12 14
 \item{sample.label}{Vector or factor. Denotes the sample label for each cell (column) in the count matrix.}
13 15
 
14 16
 \item{z}{Numeric vector. Denotes cell population labels.}
15 17
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{logLikelihoodHistory,celdaModel-method}
5
+\alias{logLikelihoodHistory,celdaModel-method}
6
+\title{Get log-likelihood history}
7
+\usage{
8
+\S4method{logLikelihoodHistory}{celdaModel}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
12
+}
13
+\value{
14
+Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.
15
+}
16
+\description{
17
+Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model.
18
+}
19
+\examples{
20
+logLikelihoodHistory(celda.CG.mod)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 logLikelihoodHistory(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
11
+}
9 12
 \value{
10 13
 Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model.
11 14
 }
12 15
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{matrixNames,celdaModel-method}
5
+\alias{matrixNames,celdaModel-method}
6
+\title{Get feature, cell and sample names from a celda model}
7
+\usage{
8
+\S4method{matrixNames}{celdaModel}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
12
+}
13
+\value{
14
+List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.
15
+}
16
+\description{
17
+Retrieves the row, column, and sample names used to generate a celda model.
18
+}
19
+\examples{
20
+matrixNames(celda.CG.mod)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 matrixNames(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
11
+}
9 12
 \value{
10 13
 List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated.
11 14
 }
12 15
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{params,celdaModel-method}
5
+\alias{params,celdaModel-method}
6
+\title{Get parameter values provided for celda model creation}
7
+\usage{
8
+\S4method{params}{celdaModel}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
12
+}
13
+\value{
14
+List. Contains the model-specific parameters for the provided celda model object depending on its class.
15
+}
16
+\description{
17
+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.
18
+}
19
+\examples{
20
+params(celda.CG.mod)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 params(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
11
+}
9 12
 \value{
10 13
 List. Contains the model-specific parameters for the provided celda model object depending on its class.
11 14
 }
... ...
@@ -9,7 +9,7 @@ perplexity(counts, celda.mod, new.counts = NULL)
9 9
 \arguments{
10 10
 \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`.}
11 11
 
12
-\item{celda.mod}{Celda object of class "celda_C", "celda_G" or "celda_CG".}
12
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
13 13
 
14 14
 \item{new.counts}{A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL.}
15 15
 }
16 16
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{resList,celdaList-method}
5
+\alias{resList,celdaList-method}
6
+\title{Get final celda models from a celdaList}
7
+\usage{
8
+\S4method{resList}{celdaList}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{An object of class celdaList.}
12
+}
13
+\value{
14
+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
15
+}
16
+\description{
17
+Returns all models generated during a `celdaGridSearch()` run.
18
+}
19
+\examples{
20
+celda.CG.grid.models = resList(celda.CG.grid.search.res)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 resList(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{An object of class celdaList.}
11
+}
9 12
 \value{
10 13
 List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list.
11 14
 }
12 15
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{runParams,celdaList-method}
5
+\alias{runParams,celdaList-method}
6
+\title{Get run parameters provided to `celdaGridSearch()`}
7
+\usage{
8
+\S4method{runParams}{celdaList}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{An object of class celdaList.}
12
+}
13
+\value{
14
+Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.
15
+}
16
+\description{
17
+Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created.
18
+}
19
+\examples{
20
+runParams(celda.CG.grid.search.res)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 runParams(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{An object of class celdaList.}
11
+}
9 12
 \value{
10 13
 Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList.
11 14
 }
12 15
new file mode 100644
... ...
@@ -0,0 +1,21 @@
1
+% Generated by roxygen2: do not edit by hand
2
+% Please edit documentation in R/all_generics.R
3
+\docType{methods}
4
+\name{sampleLabel,celdaModel-method}
5
+\alias{sampleLabel,celdaModel-method}
6
+\title{Get sample labels from a celda model}
7
+\usage{
8
+\S4method{sampleLabel}{celdaModel}(celda.mod)
9
+}
10
+\arguments{
11
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
12
+}
13
+\value{
14
+Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.
15
+}
16
+\description{
17
+Returns the sample labels for the count matrix provided for generation of a given celda model.
18
+}
19
+\examples{
20
+sampleLabel(celda.CG.mod)
21
+}
... ...
@@ -6,6 +6,9 @@
6 6
 \usage{
7 7
 sampleLabel(celda.mod)
8 8
 }
9
+\arguments{
10
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.}
11
+}
9 12
 \value{
10 13
 Character. Contains the sample labels provided at model creation time, or those automatically generated by celda.
11 14
 }