@@ -1,16 +1,18 @@

 # Generated by roxygen2: do not edit by hand

-export("clusters<-")

+export("celdaClusters<-")

+export("celdaModules<-")

 export("decontXcounts<-")

-export("modules<-")

 export("sampleLabel<-")

 export(appendCeldaList)

 export(availableModels)

 export(bestLogLikelihood)

 export(celda)

+export(celdaClusters)

 export(celdaGridSearch)

 export(celdaHeatmap)

 export(celdaModel)

+export(celdaModules)

 export(celdaPerplexity)

 export(celdaProbabilityMap)

 export(celdaTsne)

@@ -20,7 +22,6 @@ export(celda_CG)

 export(celda_G)

 export(celdatosce)

 export(clusterProbability)

-export(clusters)

 export(compareCountMatrix)

 export(countChecksum)

 export(decontX)

@@ -37,7 +38,6 @@ export(logLikelihood)

 export(logLikelihoodHistory)

 export(matrixNames)

 export(moduleHeatmap)

-export(modules)

 export(normalizeCounts)

 export(params)

 export(perplexity)

@@ -50,9 +50,6 @@ export(plotDimReduceFeature)

 export(plotDimReduceGrid)

 export(plotDimReduceModule)

 export(plotGridSearchPerplexity)

-export(plotGridSearchPerplexitycelda_C)

-export(plotGridSearchPerplexitycelda_CG)

-export(plotGridSearchPerplexitycelda_G)

 export(plotHeatmap)

 export(plotMarkerDendro)

 export(plotMarkerHeatmap)

@@ -70,14 +67,16 @@ export(simulateCells)

 export(simulateContamination)

 export(subsetCeldaList)

 export(topRank)

-exportMethods("clusters<-")

+exportMethods("celdaClusters<-")

+exportMethods("celdaModules<-")

 exportMethods("decontXcounts<-")

-exportMethods("modules<-")

 exportMethods("sampleLabel<-")

 exportMethods(bestLogLikelihood)

+exportMethods(celdaClusters)

 exportMethods(celdaGridSearch)

 exportMethods(celdaHeatmap)

 exportMethods(celdaModel)

+exportMethods(celdaModules)

 exportMethods(celdaPerplexity)

 exportMethods(celdaProbabilityMap)

 exportMethods(celdaTsne)

@@ -87,19 +86,30 @@ exportMethods(celda_CG)

 exportMethods(celda_G)

 exportMethods(celdatosce)

 exportMethods(clusterProbability)

-exportMethods(clusters)

 exportMethods(countChecksum)

 exportMethods(decontX)

 exportMethods(decontXcounts)

+exportMethods(differentialExpression)

 exportMethods(factorizeMatrix)

 exportMethods(featureModuleLookup)

+exportMethods(findMarkersTree)

+exportMethods(geneSetEnrich)

 exportMethods(logLikelihood)

 exportMethods(logLikelihoodHistory)

 exportMethods(matrixNames)

-exportMethods(modules)

+exportMethods(moduleHeatmap)

 exportMethods(params)

 exportMethods(perplexity)

+exportMethods(plotCeldaViolin)

+exportMethods(plotDimReduceCluster)

+exportMethods(plotDimReduceFeature)

+exportMethods(plotDimReduceGrid)

+exportMethods(plotDimReduceModule)

+exportMethods(plotGridSearchPerplexity)

+exportMethods(recursiveSplitCell)

+exportMethods(recursiveSplitModule)

 exportMethods(resList)

+exportMethods(resamplePerplexity)

 exportMethods(runParams)

 exportMethods(sampleLabel)

 exportMethods(selectBestModel)

@@ -121,9 +131,6 @@ importFrom(MCMCprecision,fit_dirichlet)

 importFrom(RColorBrewer,brewer.pal)

 importFrom(Rtsne,Rtsne)

 importFrom(S4Vectors,mcols)

-importFrom(SummarizedExperiment,assay)

-importFrom(SummarizedExperiment,assayNames)

-importFrom(SummarizedExperiment,colData)

 importFrom(data.table,as.data.table)

 importFrom(digest,digest)

 importFrom(doParallel,registerDoParallel)

@@ -265,19 +265,19 @@ setMethod("moduleHeatmap",

         if (class(celdaMod)[1] == "celda_CG") {

             if (methods::.hasSlot(celdaMod, "clusters")) {

                 cell <-

-                    distinctColors(length(unique(celdaMod@clusters$z)))[

-                        sort(unique(celdaMod@clusters$z[cellIx]))

+                    distinctColors(length(unique(celdaClusters(celdaMod)$z)))[

+                        sort(unique(celdaClusters(celdaMod)$z[cellIx]))

                         ]

-                names(cell) <- sort(unique(celdaMod@clusters$z[cellIx]))

+                names(cell) <- sort(unique(celdaClusters(celdaMod)$z[cellIx]))

                 anno_cell_colors <- list(cell = cell)

-                zToPlot <- celdaMod@clusters$z[cellIndices]

+                zToPlot <- celdaClusters(celdaMod)$z[cellIndices]

             }

         }

         plt <- plotHeatmap(

             filteredNormCounts,

             z = zToPlot,

-            y = celdaMod@clusters$y[geneIx],

+            y = celdaClusters(celdaMod)$y[geneIx],

             scaleRow = scaleRow,

             colorScheme = "divergent",

             showNamesFeature = showFeaturenames,

@@ -682,7 +682,7 @@ setMethod("featureModuleLookup", signature(sce = "SingleCellExperiment"),

     }

     for (x in seq(length(feature))) {

         if (feature[x] %in% rownames(sce)) {

-            list[x] <- modules(sce)[which(rownames(sce) ==

+            list[x] <- celdaModules(sce)[which(rownames(sce) ==

                     feature[x])]

         } else {

             list[x] <- paste0(

@@ -711,7 +711,7 @@ setMethod("featureModuleLookup", signature(sce = "SingleCellExperiment"),

         seq(length(feature)),

         function(x) {

             if (feature[x] %in% rownames(sce)) {

-                return(modules(sce)[which(rownames(sce) ==

+                return(celdaModules(sce)[which(rownames(sce) ==

                         feature[x])])

             } else {

                 return(paste0(

@@ -47,7 +47,7 @@

 #'  \link[S4Vectors]{metadata} \code{"celda_grid_search"} slot.

 #' @seealso \link{celda_G} for feature clustering, \link{celda_C} for

 #'  clustering of cells, and \link{celda_CG} for simultaneous clustering of

-#'  features and cells. \link{subsetCeldaList} can subset the \link{celdaList}

+#'  features and cells. \link{subsetCeldaList} can subset the \code{celdaList}

 #'  object. \link{selectBestModel} can get the best model for each combination

 #'  of parameters.

 #' @import foreach

@@ -606,6 +606,7 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

 )

+#' @rdname selectBestModel

 #' @examples

 #' data(celdaCGGridSearchRes)

 #' ## Returns same result as running celdaGridSearch with "bestOnly = TRUE"

@@ -2729,7 +2729,7 @@ subUnderscore <- function(x, n) {

 #' # Get features matrix and cluster assignments

 #' factorized <- factorizeMatrix(sim_counts$counts, cm)

 #' features <- factorized$proportions$cell

-#' class <- clusters(cm)$z

+#' class <- celdaClusters(cm)

 #'

 #' # Generate Decision Tree

 #' DecTree <- findMarkersTree(features, class, threshold = 1)

@@ -81,7 +81,7 @@

     verbose = FALSE,

     reorder = FALSE

   )

-  overallZ <- as.integer(as.factor(res@clusters$z))

+  overallZ <- as.integer(as.factor(celdaClusters(res)$z))

   currentK <- max(overallZ)

   counter <- 0

@@ -112,7 +112,7 @@

         splitOnIter = -1,

         splitOnLast = FALSE,

         verbose = FALSE)

-      tempZ <- as.integer(as.factor(clustLabel@clusters$z))

+      tempZ <- as.integer(as.factor(celdaClusters(clustLabel)$z))

       # Reassign clusters with label > 1

       splitIx <- tempZ > 1

@@ -245,7 +245,7 @@

     splitOnLast = FALSE,

     verbose = FALSE,

     reorder = FALSE)

-  overallY <- as.integer(as.factor(res@clusters$y))

+  overallY <- as.integer(as.factor(celdaClusters(res)$y))

   currentL <- max(overallY)

   counter <- 0

@@ -281,7 +281,7 @@

         splitOnLast = FALSE,

         verbose = FALSE

       )

-      tempY <- as.integer(as.factor(clustLabel@clusters$y))

+      tempY <- as.integer(as.factor(celdaClusters(clustLabel)$y))

       # Reassign clusters with label > 1

       splitIx <- tempY > 1

@@ -67,7 +67,6 @@ setMethod("resamplePerplexity",

 #' @rdname resamplePerplexity

 #' @examples

-#' @examples

 #' data(celdaCGSim, celdaCGGridSearchRes)

 #' celdaCGGridSearchRes <- resamplePerplexity(

 #'   celdaCGSim$counts,

@@ -185,7 +185,7 @@ setMethod(

             newCounts,

             phi,

             psi,

-            clusters(celdaMod)$y,

+            celdaClusters(celdaMod)$y,

             params(celdaMod)$L

         ) # + sum(etaProb)

         perplexity <- exp(- (logPx / sum(newCounts)))

@@ -1196,10 +1196,9 @@ setMethod("recursiveSplitModule",

       gamma = gamma,

       delta = delta,

       verbose = FALSE,

-      reorder = reorder

-    )

-    currentL <- length(unique(celdaClusters(modelInitial)y)) + 1

-    overallY <- celdaClusters(modelInitial)y

+      reorder = reorder)

+    currentL <- length(unique(celdaClusters(modelInitial)$y)) + 1

+    overallY <- celdaClusters(modelInitial)$y

     resList <- list(modelInitial)

     while (currentL <= maxL) {

similarity index 69%

rename from man/clusters.Rd

rename to man/celdaClusters.Rd

@@ -1,24 +1,24 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/accessors.R

-\name{clusters}

-\alias{clusters}

-\alias{clusters,SingleCellExperiment-method}

-\alias{clusters,celdaModel-method}

-\alias{clusters<-}

-\alias{clusters<-,SingleCellExperiment-method}

+\name{celdaClusters}

+\alias{celdaClusters}

+\alias{celdaClusters,SingleCellExperiment-method}

+\alias{celdaClusters,celdaModel-method}

+\alias{celdaClusters<-}

+\alias{celdaClusters<-,SingleCellExperiment-method}

 \title{Get or set the cell cluster labels from a celda

  \linkS4class{SingleCellExperiment}  object or celda model

  object.}

 \usage{

-clusters(x)

+celdaClusters(x)

-\S4method{clusters}{SingleCellExperiment}(x)

+\S4method{celdaClusters}{SingleCellExperiment}(x)

-\S4method{clusters}{celdaModel}(x)

+\S4method{celdaClusters}{celdaModel}(x)

-clusters(x) <- value

+celdaClusters(x) <- value

-\S4method{clusters}{SingleCellExperiment}(x) <- value

+\S4method{celdaClusters}{SingleCellExperiment}(x) <- value

 }

 \arguments{

 \item{x}{Can be one of

@@ -45,7 +45,7 @@ Return or set the cell cluster labels determined

 }

 \examples{

 data(sceCeldaCG)

-clusters(sceCeldaCG)

+celdaClusters(sceCeldaCG)

 data(celdaCGMod)

-clusters(celdaCGMod)

+celdaClusters(celdaCGMod)

 }

@@ -75,12 +75,12 @@ TRUE.}

 \item{seed}{Integer. Passed to \link[withr]{with_seed}. For reproducibility,

 a default value of 12345 is used. Seed values

 \code{seq(seed, (seed + nchains - 1))} will be supplied to each chain in

-\code{nchains} If NULL, no calls to

+\code{nchains}. If NULL, no calls to

 \link[withr]{with_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.}

+\link{resamplePerplexity}. Default TRUE.}

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

 execution. Default TRUE.}

similarity index 66%

rename from man/modules.Rd

rename to man/celdaModules.Rd

@@ -1,20 +1,20 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/accessors.R

-\name{modules}

-\alias{modules}

-\alias{modules,SingleCellExperiment-method}

-\alias{modules<-}

-\alias{modules<-,SingleCellExperiment-method}

+\name{celdaModules}

+\alias{celdaModules}

+\alias{celdaModules,SingleCellExperiment-method}

+\alias{celdaModules<-}

+\alias{celdaModules<-,SingleCellExperiment-method}

 \title{Get or set the feature module labels from a celda

  \linkS4class{SingleCellExperiment}  object.}

 \usage{

-modules(sce)

+celdaModules(sce)

-\S4method{modules}{SingleCellExperiment}(sce)

+\S4method{celdaModules}{SingleCellExperiment}(sce)

-modules(sce) <- value

+celdaModules(sce) <- value

-\S4method{modules}{SingleCellExperiment}(sce) <- value

+\S4method{celdaModules}{SingleCellExperiment}(sce) <- value

 }

 \arguments{

 \item{sce}{A \linkS4class{SingleCellExperiment} object returned by

@@ -32,5 +32,5 @@ Return or set the feature module cluster labels determined

 }

 \examples{

 data(celdaCGMod)

-modules(celdaCGMod)

+celdaModules(celdaCGMod)

 }

@@ -43,7 +43,7 @@ Default NULL.}

 threshold. Default 100.}

 \item{modules}{Integer vector. Determines which features modules to use for

-tSNE. If NULL, all modules will be used. Default NULL.}

+UMAP. If NULL, all modules will be used. Default NULL.}

 \item{seed}{Integer. Passed to \link[withr]{with_seed}. For reproducibility,

 a default value of 12345 is used. If NULL, no calls to

@@ -67,7 +67,7 @@ Rows represent features and columns represent cells.}

 \item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

 slot to use if \code{x} is a

-\link[SingleCellExperiment]{SingleCellExperiment} object. Default "counts".}

+\linkS4class{SingleCellExperiment} object. Default "counts".}

 \item{sampleLabel}{Vector or factor. Denotes the sample label for each cell

 (column) in the count matrix.}

@@ -2,10 +2,24 @@

 % Please edit documentation in R/diffExp.R

 \name{differentialExpression}

 \alias{differentialExpression}

+\alias{differentialExpression,SingleCellExperiment-method}

+\alias{differentialExpression,matrix-method}

 \title{Differential expression for cell subpopulations using MAST}

 \usage{

-differentialExpression(

-  counts,

+differentialExpression(x, ...)

+

+\S4method{differentialExpression}{SingleCellExperiment}(

+  x,

+  useAssay = "counts",

+  c1,

+  c2 = NULL,

+  onlyPos = FALSE,

+  log2fcThreshold = NULL,

+  fdrThreshold = 1

+)

+

+\S4method{differentialExpression}{matrix}(

+  x,

   celdaMod,

   c1,

   c2 = NULL,

@@ -15,11 +29,16 @@ differentialExpression(

 )

 }

 \arguments{

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

-cells. This matrix should be the same as the one used to generate

-`celdaMod`.}

+\item{x}{A numeric \link{matrix} of counts or a

+\linkS4class{SingleCellExperiment}

+with the matrix located in the assay slot under \code{useAssay}.

+Rows represent features and columns represent cells. Must contain cluster

+labels in \code{celdaClusters(x)} if \code{x} is a

+\linkS4class{SingleCellExperiment} object.}

-\item{celdaMod}{Celda object of class `celda_C` or `celda_CG`.}

+\item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

+slot to use if \code{x} is a

+\link[SingleCellExperiment]{SingleCellExperiment} object. Default "counts".}

 \item{c1}{Integer vector. Cell populations to include in group 1 for the

 differential expression analysis.}

@@ -39,6 +58,8 @@ applied. Default NULL.}

 \item{fdrThreshold}{Numeric. A number between 0 and 1 that specifies the

 false discovery rate (FDR) threshold. Only features below this threshold

 will be returned. Default 1.}

+

+\item{celdaMod}{Celda object of class `celda_C` or `celda_CG`.}

 }

 \value{

 Data frame containing MAST results including statistics such as

@@ -49,9 +70,10 @@ Uses MAST to find differentially expressed features for

  specified cell subpopulations.

 }

 \examples{

+data(sceCeldaCG)

+clusterDiffexpRes <- differentialExpression(sceCeldaCG, c1 = c(1, 2))

 data(celdaCGSim, celdaCGMod)

 clusterDiffexpRes <- differentialExpression(celdaCGSim$counts,

   celdaCGMod,

-  c1 = c(1, 2)

-)

+  c1 = c(1, 2))

 }

@@ -2,10 +2,31 @@

 % Please edit documentation in R/findMarkersTree.R

 \name{findMarkersTree}

 \alias{findMarkersTree}

+\alias{findMarkersTree,SingleCellExperiment-method}

+\alias{findMarkersTree,matrix-method}

 \title{Generate marker decision tree from single-cell clustering output}

 \usage{

-findMarkersTree(

-  features,

+findMarkersTree(x, ...)

+

+\S4method{findMarkersTree}{SingleCellExperiment}(

+  x,

+  useAssay = "counts",

+  class,

+  oneoffMetric = c("modified F1", "pairwise AUC"),

+  metaclusters,

+  featureLabels,

+  counts,

+  seurat,

+  threshold = 0.9,

+  reuseFeatures = FALSE,

+  altSplit = TRUE,

+  consecutiveOneoff = FALSE,

+  autoMetaclusters = TRUE,

+  seed = 12345

+)

+

+\S4method{findMarkersTree}{matrix}(

+  x,

   class,

   oneoffMetric = c("modified F1", "pairwise AUC"),

   metaclusters,

@@ -22,7 +43,14 @@ findMarkersTree(

 )

 }

 \arguments{

-\item{features}{features-by-samples numeric matrix, e.g. counts matrix.}

+\item{x}{A numeric \link{matrix} of counts or a

+\linkS4class{SingleCellExperiment}

+with the matrix located in the assay slot under \code{useAssay}.

+Rows represent features and columns represent cells.}

+

+\item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

+slot to use if \code{x} is a

+\link[SingleCellExperiment]{SingleCellExperiment} object. Default "counts".}

 \item{class}{Vector of cell cluster labels.}

@@ -41,10 +69,8 @@ modules).}

 \item{counts}{Numeric counts matrix. Useful when using clusters

 of features (e.g. gene modules) and user wishes to expand tree results to

 individual features (e.g. score individual genes within marker gene

-modules). Row names should be individual feature names.}

-

-\item{celda}{A \emph{celda_CG} or \emph{celda_C} object.

-Counts matrix has to be provided as well.}

+modules). Row names should be individual feature names. Ignored if

+\code{x} is a \linkS4class{SingleCellExperiment} object.}

 \item{seurat}{A seurat object. Note that the seurat functions

 \emph{RunPCA} and \emph{FindClusters} must have been run on the object.}

@@ -68,6 +94,9 @@ cluster that includes several clusters within it. Default is TRUE.}

 \item{seed}{Numeric. Seed used to enable reproducible UMAP results

 for identifying metaclusters. Default is 12345.}

+

+\item{celda}{A \emph{celda_CG} or \emph{celda_C} object.

+Counts matrix has to be provided as well.}

 }

 \value{

 A named list with six elements:

@@ -122,15 +151,15 @@ Create a decision tree that identifies gene markers for given

 \examples{

 \dontrun{

 # Generate simulated single-cell dataset using celda

-sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)

+sim_counts <- simulateCells("celda_CG", K = 4, L = 10, G = 100)

 # Celda clustering into 5 clusters & 10 modules

-cm <- celda_CG(sim_counts$counts, K = 5, L = 10, verbose = FALSE)

+cm <- celda_CG(sim_counts, K = 5, L = 10, verbose = FALSE)

 # Get features matrix and cluster assignments

-factorized <- factorizeMatrix(sim_counts$counts, cm)

+factorized <- factorizeMatrix(cm)

 features <- factorized$proportions$cell

-class <- clusters(cm)$z

+class <- celdaClusters(cm)

 # Generate Decision Tree

 DecTree <- findMarkersTree(features, class)

@@ -2,22 +2,35 @@

 % Please edit documentation in R/geneSetEnrich.R

 \name{geneSetEnrich}

 \alias{geneSetEnrich}

+\alias{geneSetEnrich,SingleCellExperiment-method}

+\alias{geneSetEnrich,matrix-method}

 \title{Gene set enrichment}

 \usage{

-geneSetEnrich(counts, celdaModel, databases, fdr = 0.05)

+geneSetEnrich(x, ...)

+

+\S4method{geneSetEnrich}{SingleCellExperiment}(x, useAssay = "counts", databases, fdr = 0.05)

+

+\S4method{geneSetEnrich}{matrix}(x, celdaModel, databases, fdr = 0.05)

 }

 \arguments{

-\item{counts}{Integer count matrix. Rows represent genes and columns

-represent cells. Row names of the matrix should be gene names.}

+\item{x}{A numeric \link{matrix} of counts or a

+\linkS4class{SingleCellExperiment}

+with the matrix located in the assay slot under \code{useAssay}.

+Rows represent features and columns represent cells. Rownames of the

+matrix or \linkS4class{SingleCellExperiment} object should be gene names.}

-\item{celdaModel}{Celda object of class `celda_G` or `celda_CG`.}

+\item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

+slot to use if \code{x} is a

+\linkS4class{SingleCellExperiment} object. Default "counts".}

 \item{databases}{Character vector. Name of reference database. Available

-databases can be viewed by running \code{enrichR::listEnrichrDbs()}.}

+databases can be viewed by \link[enrichR]{listEnrichrDbs}.}

 \item{fdr}{False discovery rate (FDR). Numeric. Cutoff value for adjusted

 p-value, terms with FDR below this value are considered significantly

 enriched.}

+

+\item{celdaModel}{Celda object of class \code{celda_G} or \code{celda_CG}.}

 }

 \value{

 List of length 'L' where each member contains the significantly

@@ -25,21 +38,20 @@ List of length 'L' where each member contains the significantly

 }

 \description{

 Identify and return significantly-enriched terms for each gene

- module in a Celda object. Performs gene set enrichment analysis for Celda

- identified modules using the enrichR package.

+ module in a Celda object or a \linkS4class{SingleCellExperiment} object.

+ Performs gene set enrichment analysis for Celda

+ identified modules using the \link[enrichR]{enrichr}.

 }

 \examples{

 library(M3DExampleData)

 counts <- M3DExampleData::Mmus_example_list$data

-# subset 100 genes for fast clustering

-counts <- counts[1500:2000, ]

+# subset 500 genes for fast clustering

+counts <- counts[seq(1501, 2000), ]

 # cluster genes into 10 modules for quick demo

-cm <- celda_G(counts = as.matrix(counts), L = 10, verbose = FALSE)

-gse <- geneSetEnrich(counts,

-  cm,

-  databases = c("GO_Biological_Process_2018", "GO_Molecular_Function_2018")

-)

+sce <- celda_G(counts = as.matrix(counts), L = 10, verbose = FALSE)

+gse <- geneSetEnrich(sce,

+  databases = c("GO_Biological_Process_2018", "GO_Molecular_Function_2018"))

 }

 \author{

-Ahmed Youssef

+Ahmed Youssef, Zhe Wang

 }

@@ -10,10 +10,11 @@ getDecisions(rules, features)

 getDecisions(rules, features)

 }

 \arguments{

-\item{rules}{List object. The `rules` element from  `findMarkers`

-output. Returns NA if cluster estimation was ambiguous.}

+\item{rules}{List object. The \code{rules} element from

+\code{findMarkersTree} output. Returns NA if cluster estimation was

+ambiguous.}

-\item{features}{A L(features) by N(samples) numeric matrix.}

+\item{features}{A L (features) by N (samples) numeric matrix.}

 }

 \value{

 A character vector of label predicitions.

@@ -32,21 +33,19 @@ Get decisions for a matrix of features. Estimate cell

 library(M3DExampleData)

 counts <- M3DExampleData::Mmus_example_list$data

 # Subset 500 genes for fast clustering

-counts <- as.matrix(counts[1501:2000, ])

-# Cluster genes ans samples each into 10 modules

-cm <- celda_CG(counts = counts, L = 10, K = 5, verbose = FALSE)

+counts <- as.matrix(counts[seq(1501, 2000), ])

+# Cluster genes and samples each into 10 modules

+sce <- celda_CG(counts = counts, L = 10, K = 5, verbose = FALSE)

 # Get features matrix and cluster assignments

-factorized <- factorizeMatrix(counts, cm)

+factorized <- factorizeMatrix(sce)

 features <- factorized$proportions$cell

-class <- clusters(cm)$z

+class <- celdaClusters(sce)

 # Generate Decision Tree

-DecTree <- findMarkers(features,

+DecTree <- findMarkersTree(features,

   class,

   oneoffMetric = "modified F1",

   threshold = 1,

-  consecutiveOneoff = FALSE

-)

-

+  consecutiveOneoff = FALSE)

 # Get sample estimates in training data

 getDecisions(DecTree$rules, features)

 }

@@ -2,10 +2,25 @@

 % Please edit documentation in R/StateHeatmap.R

 \name{moduleHeatmap}

 \alias{moduleHeatmap}

+\alias{moduleHeatmap,SingleCellExperiment-method}

+\alias{moduleHeatmap,matrix-method}

 \title{Heatmap for featureModules}

 \usage{

-moduleHeatmap(

-  counts,

+moduleHeatmap(x, ...)

+

+\S4method{moduleHeatmap}{SingleCellExperiment}(

+  x,

+  useAssay = "counts",

+  featureModule = 1,

+  topCells = 100,

+  topFeatures = NULL,

+  normalizedCounts = NA,

+  scaleRow = scale,

+  showFeaturenames = TRUE

+)

+

+\S4method{moduleHeatmap}{matrix}(

+  x,

   celdaMod,

   featureModule = 1,

   topCells = 100,

@@ -16,11 +31,14 @@ moduleHeatmap(

 )

 }

 \arguments{

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

-cells. This matrix should be the same as the one used to generate

-`celdaMod`.}

+\item{x}{A numeric \link{matrix} of counts or a

+\linkS4class{SingleCellExperiment}

+with the matrix located in the assay slot under \code{useAssay}.

+Rows represent features and columns represent cells.}

-\item{celdaMod}{Celda object of class `celda_G` or `celda_CG`.}

+\item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

+slot to use if \code{x} is a

+\linkS4class{SingleCellExperiment} object. Default "counts".}

 \item{featureModule}{Integer Vector. The featureModule(s) to display.

 Multiple modules can be included in a vector.}

@@ -50,6 +68,9 @@ Default `scale`.}

 \item{showFeaturenames}{Logical. Wheter feature names should be displayed.

 Default TRUE.}

+

+\item{celdaMod}{Celda object of class \link{celda_G} or \link{celda_CG}. Used

+only if \code{x} is a matrix object.}

 }

 \value{

 A list containing row and column dendrograms as well as a gtable for

@@ -66,4 +87,6 @@ Renders a heatmap for selected featureModules. Cells are

 \examples{

 data(celdaCGSim, celdaCGMod)

 moduleHeatmap(celdaCGSim$counts, celdaCGMod)

+data(celdaCGSim, celdaCGMod)

+moduleHeatmap(celdaCGSim$counts, celdaCGMod)

 }

@@ -2,10 +2,23 @@

 % Please edit documentation in R/plot_dr.R

 \name{plotCeldaViolin}

 \alias{plotCeldaViolin}

+\alias{plotCeldaViolin,SingleCellExperiment-method}

+\alias{plotCeldaViolin,matrix-method}

 \title{Feature Expression Violin Plot}

 \usage{

-plotCeldaViolin(

-  counts,

+plotCeldaViolin(x, ...)

+

+\S4method{plotCeldaViolin}{SingleCellExperiment}(

+  x,

+  useAssay = "counts",

+  features,

+  exactMatch = TRUE,

+  plotDots = TRUE,

+  dotSize = 0.1

+)

+

+\S4method{plotCeldaViolin}{matrix}(

+  x,

   celdaMod,

   features,

   exactMatch = TRUE,

@@ -14,10 +27,13 @@ plotCeldaViolin(

 )

 }

 \arguments{

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

-cells.}

+\item{x}{Numeric matrix or a \linkS4class{SingleCellExperiment} object

+with the matrix located in the assay slot under \code{useAssay}. Rows

+represent features and columns represent cells.}

-\item{celdaMod}{Celda object of class "celda_G" or "celda_CG".}

+\item{useAssay}{A string specifying which \link[SummarizedExperiment]{assay}

+slot to use if \code{x} is a

+\linkS4class{SingleCellExperiment} object. Default "counts".}

 \item{features}{Character vector. Uses these genes for plotting.}

@@ -31,6 +47,9 @@ curve. Default \code{TRUE}.}

 \item{dotSize}{Numeric. Size of points if \code{plotDots = TRUE}.

 Default \code{0.1}.}

+

+\item{celdaMod}{Celda object of class "celda_G" or "celda_CG". Used only if

+\code{x} is a matrix object.}

 }

 \value{

 Violin plot for each feature, grouped by celda cluster

@@ -39,9 +58,9 @@ Violin plot for each feature, grouped by celda cluster

 Outputs a violin plot for feature expression data.

 }

 \examples{

+data(sceCeldaCG)

+plotCeldaViolin(x = sceCeldaCG, features = "Gene_1")

 data(celdaCGSim, celdaCGMod)

-plotCeldaViolin(

-  counts = celdaCGSim$counts,

-  celdaMod = celdaCGMod, features = "Gene_1"

-)

+plotCeldaViolin(counts = celdaCGSim$counts, celdaMod = celdaCGMod,

+   features = "Gene_1")

 }

@@ -2,12 +2,29 @@

 % Please edit documentation in R/plot_dr.R

 \name{plotDimReduceCluster}

 \alias{plotDimReduceCluster}

+\alias{plotDimReduceCluster,SingleCellExperiment-method}

+\alias{plotDimReduceCluster,vector-method}

 \title{Plotting the cell labels on a dimensionality reduction plot}

 \usage{

-plotDimReduceCluster(

+plotDimReduceCluster(x, ...)

+

+\S4method{plotDimReduceCluster}{SingleCellExperiment}(

+  dim1,

+  dim2,

+  x,

+  size = 1,

+  xlab = "Dimension_1",

+  ylab = "Dimension_2",

+  specificClusters = NULL,

+  labelClusters = FALSE,

+  groupBy = NULL,

+  labelSize = 3.5

+)

+

+\S4method{plotDimReduceCluster}{vector}(

   dim1,

   dim2,

-  cluster,

+  x,

   size = 1,

   xlab = "Dimension_1",

   ylab = "Dimension_2",

@@ -18,14 +35,17 @@ plotDimReduceCluster(

 )

 }

 \arguments{

+\item{x}{Integer vector of cell cluster labels or a

+\linkS4class{SingleCellExperiment} object

+containing cluster labels for each cell in \code{"celda_cell_cluster"}

+column in \code{colData(x)}.}

+

 \item{dim1}{Numeric vector. First dimension from data

 dimensionality reduction output.}

 \item{dim2}{Numeric vector. Second dimension from data

 dimensionality reduction output.}

-\item{cluster}{Integer vector. Contains cluster labels for each cell.}

-

 \item{size}{Numeric. Sets size of point on plot. Default 1.}

 \item{xlab}{Character vector. Label for the x-axis. Default "Dimension_1".}

@@ -45,6 +65,8 @@ If NULL, all samples will be plotted together. Default NULL.}

 \item{labelSize}{Numeric. Sets size of label if labelClusters is TRUE.

 Default 3.5.}

+

+\item{cluster}{Integer vector. Contains cluster labels for each cell.}

 }

 \value{

 The plot as a ggplot object

@@ -57,16 +79,21 @@ Create a scatterplot for each row of a normalized

 }

 \examples{

 \donttest{

-data(celdaCGSim, celdaCGMod)

-celdaTsne <- celdaTsne(

-  counts = celdaCGSim$counts,

-  celdaMod = celdaCGMod

-)

+data(sceCeldaCG)

+celdaTsne <- celdaTsne(sceCeldaCG)

 plotDimReduceCluster(

   dim1 = celdaTsne[, 1],

   dim2 = celdaTsne[, 2],

-  cluster = as.factor(celdaCGMod@clusters$z),

-  specificClusters = c(1, 2, 3)

-)

+  x = sceCeldaCG,

+  specificClusters = c(1, 2, 3))

+}

+\donttest{

+data(sceCeldaCG, celdaCGMod)

+celdaTsne <- celdaTsne(sceCeldaCG)

+plotDimReduceCluster(

+  dim1 = celdaTsne[, 1],