@@ -10,6 +10,7 @@ export(celdaHeatmap)

 export(celdaPerplexity)

 export(celdaProbabilityMap)

 export(celdaTsne)

+export(celdaUmap)

 export(celda_C)

 export(celda_CG)

 export(celda_G)

@@ -59,6 +60,7 @@ export(violinPlot)

 exportMethods(celdaHeatmap)

 exportMethods(celdaProbabilityMap)

 exportMethods(celdaTsne)

+exportMethods(celdaUmap)

 exportMethods(clusterProbability)

 exportMethods(factorizeMatrix)

 exportMethods(featureModuleLookup)

@@ -617,55 +617,94 @@ setMethod("celdaTsne",

                      initial.dims=20, modules=NULL, perplexity=20, max.iter=2500, 

                      seed=12345, ...) {

-            counts = processCounts(counts)

-            compareCountMatrix(counts, celda.mod)

-            

-            ## Checking if max.cells and min.cluster.size will work

-            if((max.cells < ncol(counts)) & (max.cells / min.cluster.size < celda.mod@params$K)) {

-              stop(paste0("Cannot distribute ", max.cells, " cells among ", 

-                          celda.mod@params$K, " clusters while maintaining a minumum of ", 

-                          min.cluster.size, 

-                          " cells per cluster. Try increasing 'max.cells' or decreasing 'min.cluster.size'."))

-            }

+

+            prepared.count.info = prepareCountsForDimReduction.celda_C(counts, celda.mod, max.cells,

+                                                                       min.cluster.size, modules)

-            ## Select a subset of cells to sample if greater than 'max.cells'

-            total.cells.to.remove = ncol(counts) - max.cells

-            z.include = rep(TRUE, ncol(counts))

-            if(total.cells.to.remove > 0) {

-          	z.ta = tabulate(celda.mod@clusters$z, celda.mod@params$K)

-          	

-          	## Number of cells that can be sampled from each cluster without 

-          	## going below the minimum threshold

-          	cluster.cells.to.sample = z.ta - min.cluster.size          

-          	cluster.cells.to.sample[cluster.cells.to.sample < 0] = 0

-          	

-          	## Number of cells to sample after exluding smaller clusters

-          	## Rounding can cause number to be off by a few, so ceiling is 

-          	## used with a second round of subtraction

-          	cluster.n.to.sample = ceiling((cluster.cells.to.sample / sum(cluster.cells.to.sample)) * total.cells.to.remove)

-          	diff = sum(cluster.n.to.sample) - total.cells.to.remove 

-          	cluster.n.to.sample[which.max(cluster.n.to.sample)] = cluster.n.to.sample[which.max(cluster.n.to.sample)] - diff

-          

-          	## Perform sampling for each cluster

-          	for(i in which(cluster.n.to.sample > 0)) {

-          	  z.include[sample(which(celda.mod@clusters$z == i), cluster.n.to.sample[i])] = FALSE

-          	}

-            }   

-            cell.ix = which(z.include)

-          

-            norm = t(normalizeCounts(counts[,cell.ix], normalize="proportion", 

-                                     transformation.fun=sqrt))

-            res = calculateTsne(norm, perplexity=perplexity, max.iter=max.iter, 

-                                seed=seed, do.pca=TRUE, 

-                                initial.dims = initial.dims)

+            res = calculateTsne(prepared.count.info$norm, perplexity=perplexity, max.iter=max.iter, 

+                                seed=seed, do.pca=TRUE, initial.dims = initial.dims)

             final = matrix(NA, nrow=ncol(counts), ncol=2)

-            final[cell.ix,] = res

+            final[prepared.couunt.info$cell.ix,] = res

             rownames(final) = colnames(counts)

             colnames(final) = c("tsne_1", "tsne_2")

             return(final)

           })

+#' @title umap for celda_C

+#' @description Embeds cells in two dimensions using umap based on a `celda_C` model. PCA on the normalized counts is used to reduce the number of features before applying umap. 

+#' 

+#' @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`. 

+#' @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 ... Additional parameters.

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

+#' @examples

+#' umap.res = celdaUmap(celda.C.sim$counts, celda.C.mod)

+#' @return A two column matrix of t-SNE coordinates

+#' @export

+setMethod("celdaUmap",

+          signature(celda.mod = "celda_C"),

+          function(counts, celda.mod, max.cells=25000, min.cluster.size=100,

+                   modules=NULL, umap.config=umap::umap.defaults) {

+            prepared.count.info = prepareCountsForDimReduction.celda_C(counts, celda.mod, max.cells,

+                                                                       min.cluster.size, modules)

+            res = calculateUmap(prepared.count.info$norm, umap.config)

+            final = matrix(NA, nrow=ncol(counts), ncol=2)

+            final[prepared.count.info$cell.ix,] = res

+            rownames(final) = colnames(counts)

+            colnames(final) = c("umap_1", "umap_2")

+            return(final)

+          })

+

+

+prepareCountsForDimReduction.celda_C = function(counts, celda.mod, max.cells=25000, min.cluster.size=100,

+                                                modules=NULL) {

+  counts = processCounts(counts)

+  compareCountMatrix(counts, celda.mod)

+  

+  ## Checking if max.cells and min.cluster.size will work

+  if((max.cells < ncol(counts)) & (max.cells / min.cluster.size < celda.mod@params$K)) {

+  stop(paste0("Cannot distribute ", max.cells, " cells among ", 

+              celda.mod@params$K, " clusters while maintaining a minumum of ", 

+              min.cluster.size, 

+              " cells per cluster. Try increasing 'max.cells' or decreasing 'min.cluster.size'."))

+  }

+  

+  ## Select a subset of cells to sample if greater than 'max.cells'

+  total.cells.to.remove = ncol(counts) - max.cells

+  z.include = rep(TRUE, ncol(counts))

+  if(total.cells.to.remove > 0) {

+    z.ta = tabulate(celda.mod@clusters$z, celda.mod@params$K)

+    

+    ## Number of cells that can be sampled from each cluster without 

+    ## going below the minimum threshold

+    cluster.cells.to.sample = z.ta - min.cluster.size          

+    cluster.cells.to.sample[cluster.cells.to.sample < 0] = 0

+    

+    ## Number of cells to sample after exluding smaller clusters

+    ## Rounding can cause number to be off by a few, so ceiling is 

+    ## used with a second round of subtraction

+    cluster.n.to.sample = ceiling((cluster.cells.to.sample / sum(cluster.cells.to.sample)) * total.cells.to.remove)

+    diff = sum(cluster.n.to.sample) - total.cells.to.remove 

+    cluster.n.to.sample[which.max(cluster.n.to.sample)] = cluster.n.to.sample[which.max(cluster.n.to.sample)] - diff

+    

+    ## Perform sampling for each cluster

+    for(i in which(cluster.n.to.sample > 0)) {

+      z.include[sample(which(celda.mod@clusters$z == i), cluster.n.to.sample[i])] = FALSE

+    }

+  }   

+  cell.ix = which(z.include)

+  norm = t(normalizeCounts(counts[,cell.ix], normalize="proportion", 

+                         transformation.fun=sqrt))

+  return(list(norm=norm, cell.ix=cell.ix))

+}

+

 #' @title Probability map for a celda_C model

 #' @description Renders probability and relative expression heatmaps to visualize the relationship between cell populations and samples.

@@ -763,7 +763,7 @@ setMethod("celdaTsne",

 #' @param umap.config Object of class `umap.config`. Configures parameters for umap. Default `umap::umap.defaults`

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

 #' @examples

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

+#' umap.res = celdaUmap(celda.CG.sim$counts, celda.CG.mod)

 #' @return A two column matrix of t-SNE coordinates

 #' @export

 setMethod("celdaUmap",

@@ -603,27 +603,9 @@ setMethod("celdaTsne",

                     initial.dims=20, modules=NULL, perplexity=20, max.iter=2500, 

                     seed=12345, ...) {

-            if(max.cells > ncol(counts)) {

-              max.cells = ncol(counts)

-            }

-            fm = factorizeMatrix(counts=counts, celda.mod=celda.mod, 

-                                 type="counts")

-              

-            modules.to.use = 1:nrow(fm$counts$cell)

-            if (!is.null(modules)) {

-          	if (!all(modules %in% modules.to.use)) {

-          	  stop("'modules' must be a vector of numbers between 1 and ", 

-          	       modules.to.use, ".")

-          	}

-          	modules.to.use = modules 

-            }

-             

-            cell.ix = sample(1:ncol(counts), max.cells)

-            norm = t(normalizeCounts(fm$counts$cell[modules.to.use,cell.ix], 

-                                     normalize="proportion", 

-                                     transformation.fun=sqrt))

-          

+            norm = prepareCountsForDimReduction.celda_G(counts, celda.mod, max.cells, 

+                                                        min.cluster.size, modules) 

             res = calculateTsne(norm, do.pca=FALSE, perplexity=perplexity, 

                                 max.iter=max.iter, seed=seed)

             rownames(res) = colnames(counts)

@@ -632,6 +614,62 @@ setMethod("celdaTsne",

           })

+#' @title umap for celda_G

+#' @description Embeds cells in two dimensions using umap based on a `celda_G` model. umap is run on module probabilities to reduce the number of features instead of using PCA. Module probabilities square-root trasformed before applying tSNE. 

+#' 

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

+#' @param umap.config Object of class `umap.config`. Configures parameters for umap. Default `umap::umap.defaults`

+#' @seealso `celda_G()` for clustering features and cells  and `celdaHeatmap()` for displaying expression

+#' @examples

+#' umap.res = celdaUmap(celda.G.sim$counts, celda.G.mod)

+#' @return A two column matrix of t-SNE coordinates

+#' @export

+setMethod("celdaUmap",

+          signature(celda.mod = "celda_G"),

+          function(counts, celda.mod, max.cells=25000, min.cluster.size=100,

+                   modules=NULL, umap.config=umap::umap.defaults) {

+            prepared.count.info = prepareCountsForDimReduction.celda_G(counts, celda.mod,

+                                                                       max.cells, min.cluster.size,

+                                                                       modules)

+            umap.res = calculateUmap(prepared.count.info$norm, umap.config)

+            final = matrix(NA, nrow=ncol(counts), ncol=2)

+            final[prepared.count.info$cell.ix, ] = umap.res

+            rownames(final) = colnames(counts)

+            colnames(final) = c("umap_1", "umap_2")

+            return(final)

+          })

+

+

+prepareCountsForDimReduction.celda_G = function(counts, celda.mod, max.cells=25000, min.cluster.size=100,

+                                                modules=NULL) {

+  if(max.cells > ncol(counts)) {

+              max.cells = ncol(counts)

+            }

+            

+  fm = factorizeMatrix(counts=counts, celda.mod=celda.mod, 

+                       type="counts")

+    

+  modules.to.use = 1:nrow(fm$counts$cell)

+  if (!is.null(modules)) {

+  if (!all(modules %in% modules.to.use)) {

+    stop("'modules' must be a vector of numbers between 1 and ", 

+         modules.to.use, ".")

+  }

+  modules.to.use = modules 

+  }

+   

+  cell.ix = sample(1:ncol(counts), max.cells)

+  norm = t(normalizeCounts(fm$counts$cell[modules.to.use,cell.ix], 

+                           normalize="proportion", 

+                           transformation.fun=sqrt))

+  return(list(norm=norm, cell.ix=cell.ix))

+}

+

+

 #' @title Lookup the module of a feature

 #' @description Finds the module assignments of given features in a `celda_G()` model

 #'  

@@ -11,7 +11,21 @@ celdaTsne(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,

 \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 model. Options available in `celda::available.models`.}

+\item{celda.mod}{Celda object of class `celda_CG`.}

+

+\item{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.}

+

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

 }

new file mode 100755

@@ -0,0 +1,42 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/celda_C.R

+\docType{methods}

+\name{celdaUmap,celda_C-method}

+\alias{celdaUmap,celda_C-method}

+\title{umap for celda_C}

+\usage{

+\S4method{celdaUmap}{celda_C}(counts, celda.mod, max.cells = 25000,

+  min.cluster.size = 100, modules = NULL,

+  umap.config = umap::umap.defaults)

+}

+\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`.}

+

+\item{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.}

+

+\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{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.}

+}

+\value{

+A two column matrix of t-SNE coordinates

+}

+\description{

+Embeds cells in two dimensions using umap based on a `celda_C` model. PCA on the normalized counts is used to reduce the number of features before applying umap.

+}

+\examples{

+umap.res = celdaUmap(celda.C.sim$counts, celda.C.mod)

+}

+\seealso{

+`celda_C()` for clustering cells and `celdaHeatmap()` for displaying expression

+}

new file mode 100755

@@ -0,0 +1,36 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/celda_CG.R

+\docType{methods}

+\name{celdaUmap,celda_CG-method}

+\alias{celdaUmap,celda_CG-method}

+\title{umap for celda_CG}

+\usage{

+\S4method{celdaUmap}{celda_CG}(counts, celda.mod, max.cells = 25000,

+  min.cluster.size = 100, modules = NULL,

+  umap.config = umap::umap.defaults)

+}

+\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_CG`.}

+

+\item{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.}

+

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

+

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

+

+\item{umap.config}{Object of class `umap.config`. Configures parameters for umap. Default `umap::umap.defaults`}

+}

+\value{

+A two column matrix of t-SNE coordinates

+}

+\description{

+Embeds cells in two dimensions using umap based on a `celda_CG` model. umap is run on module probabilities to reduce the number of features instead of using PCA. Module probabilities square-root trasformed before applying tSNE.

+}

+\examples{

+umap.res = celdaUmap(celda.CG.sim$counts, celda.CG.mod)

+}

+\seealso{

+`celda_CG()` for clustering features and cells  and `celdaHeatmap()` for displaying expression

+}

new file mode 100755

@@ -0,0 +1,36 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/celda_G.R

+\docType{methods}

+\name{celdaUmap,celda_G-method}

+\alias{celdaUmap,celda_G-method}

+\title{umap for celda_G}

+\usage{

+\S4method{celdaUmap}{celda_G}(counts, celda.mod, max.cells = 25000,

+  min.cluster.size = 100, modules = NULL,

+  umap.config = umap::umap.defaults)

+}

+\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_CG`.}

+

+\item{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.}

+

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

+

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

+

+\item{umap.config}{Object of class `umap.config`. Configures parameters for umap. Default `umap::umap.defaults`}

+}

+\value{

+A two column matrix of t-SNE coordinates

+}

+\description{

+Embeds cells in two dimensions using umap based on a `celda_G` model. umap is run on module probabilities to reduce the number of features instead of using PCA. Module probabilities square-root trasformed before applying tSNE.

+}

+\examples{

+umap.res = celdaUmap(celda.G.sim$counts, celda.G.mod)

+}

+\seealso{

+`celda_G()` for clustering features and cells  and `celdaHeatmap()` for displaying expression

+}

new file mode 100755

@@ -0,0 +1,39 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/all_generics.R

+\name{celdaUmap}

+\alias{celdaUmap}

+\title{Embeds cells in two dimensions using umap.}

+\usage{

+celdaUmap(counts, celda.mod, max.cells = 25000, min.cluster.size = 100,

+  modules = NULL, umap.config = umap::umap.defaults)

+}

+\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_CG`.}

+

+\item{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.}

+

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

+}

+\value{

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

+}

+\description{

+Embeds cells in two dimensions using umap.

+}

+\examples{

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

+}

@@ -231,6 +231,27 @@ test_that(desc = "Testing celdaTsne with celda_C including a subset of cells",{

   expect_true(!is.null(plot.obj))

 })

+test_that(desc = "Testing celdaUmap with celda_C when model class is changed, should error",{

+  model_X <- model_C

+  class(model_X) <- "celda_X"

+  expect_error(celdaUmap(counts=celdaC.sim$counts, celda.mod=model_X, max.cells=length(model_C@clusters$z), min.cluster.size=10), "unable to find")

+})

+

+test_that(desc = "Testing celdaUmap with celda_C including all cells",{

+  umap = celdaUmap(counts=celdaC.sim$counts, celda.mod=model_C, max.cells=length(model_C@clusters$z), min.cluster.size=10)

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], model_C@clusters$z)

+  expect_true(ncol(umap) == 2 & nrow(umap) == length(model_C@clusters$z))

+  expect_true(!is.null(plot.obj))

+})

+

+test_that(desc = "Testing celdaUmap with celda_C including a subset of cells",{

+  expect_success(expect_error(umap <- celdaUmap(counts=celdaC.sim$counts, celda.mod=model_C, max.cells=50, min.cluster.size=50)))

+  umap <- celdaUmap(counts=celdaC.sim$counts, celda.mod=model_C, max.cells=100, min.cluster.size=10)

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], model_C@clusters$z)

+  expect_true(ncol(umap) == 2 & nrow(umap) == length(model_C@clusters$z) && sum(!is.na(umap[,1])) == 100)

+  expect_true(!is.null(plot.obj))

+})

+

 # featureModuleLookup

 test_that(desc = "Testing featureModuleLookup with celda_C", {

   expect_error(featureModuleLookup(celdaC.sim$counts, model_C, "test_feat"))

@@ -343,6 +343,32 @@ test_that(desc = "Testing celdaTsne.celda_CG with subset of cells",{

   expect_true(!is.null(plot.obj))

 })

+# celdaUmap

+test_that(desc = "Testing celdaUmap with celda_CG when model class is changed, should error",{

+  model_X <- model_CG

+  class(model_X) <- "celda_X"

+  expect_error(celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_X),

+               "unable to find")

+})

+

+test_that(desc = "Testing celdaUmap.celda_CG with all cells",{

+  umap = celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_CG, max.cells=length(model_CG@clusters$z))

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], model_CG@clusters$z)

+  expect_true(ncol(umap) == 2 & nrow(umap) == length(model_CG@clusters$z))

+  expect_true(!is.null(plot.obj))

+  

+  umap = celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_CG, max.cells=ncol(celdaCG.sim$counts), modules=1:2)

+  expect_error(umap <- celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_CG, max.cells=ncol(celdaCG.sim$counts), modules=1000:1005))

+})

+

+test_that(desc = "Testing celdaUmap.celda_CG with subset of cells",{

+  expect_success(expect_error(umap <- celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_CG, max.cells=50, min.cluster.size=50)))

+  umap = celdaUmap(counts=celdaCG.sim$counts, celda.mod=model_CG, max.cells=100, min.cluster.size=10)

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], model_CG@clusters$z)

+  expect_true(ncol(umap) == 2 & nrow(umap) == length(model_CG@clusters$z) && sum(!is.na(umap[,1])) == 100)

+  expect_true(!is.null(plot.obj))

+})

+

 # featureModuleLookup

 test_that(desc = "Testing featureModuleLookup with celda_CG", {

   res = featureModuleLookup(celdaCG.sim$counts, model_CG, "Gene_1")

@@ -230,6 +230,30 @@ test_that(desc = "Testing celdaTsne with celda_G including a subset of cells",{

   expect_true(!is.null(plot.obj))

 })

+#celdaUmap

+test_that(desc = "Testing celdaUmap with celda_G when model class is changed, should error",{

+  model_X <- model_G

+  class(model_X) <- "celda_X"

+  expect_error(celdaUmap(counts=celdaG.sim$counts, celda.mod=model_X), "unable to find")

+})

+

+test_that(desc = "Testing celdaUmap with celda_C including all cells",{

+  umap = celdaUmap(counts=celdaG.sim$counts, celda.mod=model_G, max.cells=ncol(celdaG.sim$counts))

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], rep(1,ncol(celdaG.sim$counts)))

+  expect_true(ncol(umap) == 2 & nrow(umap) == ncol(celdaG.sim$counts))

+  expect_true(!is.null(plot.obj))

+  

+  umap = celdaUmap(counts=celdaG.sim$counts, celda.mod=model_G, max.cells=ncol(celdaG.sim$counts), modules=1:2)

+  expect_error(umap <- celdaUmap(counts=celdaG.sim$counts, celda.mod=model_G, max.cells=ncol(celdaG.sim$counts), modules=1000:1005))

+})

+

+test_that(desc = "Testing celdaUmap with celda_G including a subset of cells",{

+  umap = celdaUmap(counts=celdaG.sim$counts, celda.mod=model_G, max.cells=100)

+  plot.obj = plotDimReduceCluster(umap[,1], umap[,2], rep(1, ncol(celdaG.sim$counts)))

+    expect_true(ncol(umap) == 2 & nrow(umap) == ncol(celdaG.sim$counts) && sum(!is.na(umap[,1])) == 100)

+  expect_true(!is.null(plot.obj))

+})

+

 # featureModuleLookup

 test_that(desc = "Testing featureModuleLookup with celda_G", {

   res = featureModuleLookup(celdaG.sim$counts, model_G, "Gene_1")