@@ -28,7 +28,7 @@ Imports:

     methods,

     reshape2,

     MAST,

-    GenomicRanges,

+    S4Vectors,

     data.table,

     Rcpp,

     RcppEigen,

@@ -38,8 +38,7 @@ Imports:

     SummarizedExperiment,

     MCMCprecision,

     ggrepel,

-    Rtsne,

-    S4Vectors

+    Rtsne

 Suggests:

     testthat,

     knitr,

@@ -76,23 +76,51 @@ exportMethods(perplexity)

 exportMethods(resList)

 exportMethods(runParams)

 exportMethods(sampleLabel)

-import(MAST, except = c(combine))

-import(RColorBrewer)

 import(Rcpp)

 import(RcppEigen)

-import(SummarizedExperiment, except = c(shift, rowRanges))

-import(data.table)

-import(foreach)

-import(grDevices)

+import(ggplot2)

 import(graphics)

-import(grid)

-import(gridExtra)

 import(gridExtra, except = c(combine))

-import(gtable)

-import(matrixStats, except = c(count))

-import(plyr)

-import(scales)

+import(stats)

+importFrom(MAST,FromMatrix)

+importFrom(MAST,summary)

+importFrom(MAST,zlm)

+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)

 importFrom(enrichR,enrichr)

+importFrom(enrichR,listEnrichrDbs)

+importFrom(foreach,foreach)

+importFrom(ggrepel,geom_text_repel)

+importFrom(grDevices,colorRampPalette)

+importFrom(grDevices,colors)

+importFrom(grDevices,hsv)

+importFrom(grDevices,rgb2hsv)

+importFrom(grid,grid.draw)

+importFrom(grid,grid.newpage)

+importFrom(grid,unit)

+importFrom(gridExtra,grid.arrange)

+importFrom(gtable,gtable)

+importFrom(gtable,gtable_add_grob)

+importFrom(gtable,gtable_height)

+importFrom(gtable,gtable_width)

+importFrom(matrixStats,logSumExp)

+importFrom(methods,.hasSlot)

+importFrom(methods,is)

+importFrom(methods,new)

+importFrom(plyr,mapvalues)

+importFrom(reshape2,melt)

+importFrom(scales,dscale)

+importFrom(stringi,stri_list2matrix)

+importFrom(umap,umap)

+importFrom(umap,umap.defaults)

 useDynLib(celda,"_colSumByGroup")

 useDynLib(celda,"_colSumByGroupChange")

 useDynLib(celda,"_colSumByGroup_numeric")

@@ -32,6 +32,7 @@

 #' @examples

 #' data(celdaCGSim, celdaCGMod)

 #' moduleHeatmap(celdaCGSim$counts, celdaCGMod)

+#' @importFrom methods .hasSlot

 #' @export

 moduleHeatmap <- function(counts,

     celdaMod,

@@ -327,6 +327,7 @@ setMethod("celdaPerplexity",

 #' data(celdaCGGridSearchRes)

 #' appendedList <- appendCeldaList(celdaCGGridSearchRes,

 #'     celdaCGGridSearchRes)

+#' @importFrom methods new

 #' @export

 appendCeldaList <- function(list1, list2) {

     if (!is.element("celdaList", class(list1)) |

@@ -579,6 +580,7 @@ setGeneric("celdaTsne",

 #' @examples

 #' data(celdaCGSim, celdaCGMod)

 #' tsneRes <- celdaUmap(celdaCGSim$counts, celdaCGMod)

+#' @importFrom umap umap.defaults

 #' @export

 setGeneric("celdaUmap",

     signature = "celdaMod",

@@ -46,7 +46,9 @@

 #'     bestOnly = TRUE,

 #'     nchains = 1,

 #'     cores = 2)

-#' @import foreach

+#' @importFrom foreach foreach

+#' @importFrom doParallel registerDoParallel

+#' @importFrom methods is

 #' @export

 celdaGridSearch <- function(counts,

     model,

@@ -296,7 +298,7 @@ subsetCeldaList <- function(celdaList, params) {

 #' data(celdaCGGridSearchRes)

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

 #' cgsBest <- selectBestModel(celdaCGGridSearchRes)

-#' @import data.table

+#' @importFrom data.table as.data.table

 #' @export

 selectBestModel <- function(celdaList) {

     if (!methods::is(celdaList, "celdaList"))

@@ -521,6 +521,7 @@ celda_C <- function(counts,

 #' @examples

 #' celdaCSim <- simulateCells(model = "celda_C", K = 10)

 #' simCounts <- celdaCSim$counts

+#' @import stats

 #' @export

 simulateCellscelda_C <- function(model,

     S = 5,

@@ -873,7 +874,7 @@ setMethod("clusterProbability", signature(celdaMod = "celda_C"),

 #' @examples

 #' data(celdaCSim, celdaCMod)

 #' perplexity <- perplexity(celdaCSim$counts, celdaCMod)

-#' @rawNamespace import(matrixStats, except = c(count))

+#' @importFrom matrixStats logSumExp

 #' @export

 setMethod("perplexity", signature(celdaMod = "celda_C"),

     function(counts, celdaMod, newCounts = NULL) {

@@ -1131,6 +1132,7 @@ setMethod("celdaUmap", signature(celdaMod = "celda_C"),

 #' data(celdaCSim, celdaCMod)

 #' celdaProbabilityMap(celdaCSim$counts, celdaCMod)

 #' @return A grob containing the specified plots

+#' @importFrom gridExtra grid.arrange

 #' @export

 setMethod("celdaProbabilityMap", signature(celdaMod = "celda_C"),

     function(counts, celdaMod, level = c("sample"), ...) {

@@ -1108,7 +1108,7 @@ setMethod("clusterProbability", signature(celdaMod = "celda_CG"),

 #' @examples

 #' data(celdaCGSim, celdaCGMod)

 #' perplexity <- perplexity(celdaCGSim$counts, celdaCGMod)

-#' @rawNamespace import(matrixStats, except = c(count))

+#' @importFrom matrixStats logSumExp

 #' @export

 setMethod("perplexity", signature(celdaMod = "celda_CG"),

     function(counts, celdaMod, newCounts = NULL) {

@@ -1412,7 +1412,9 @@ setMethod("celdaUmap",

 #' data(celdaCGSim, celdaCGMod)

 #' celdaProbabilityMap(celdaCGSim$counts, celdaCGMod)

 #' @return A grob containing the specified plots

-#' @import gridExtra

+#' @importFrom gridExtra grid.arrange

+#' @importFrom RColorBrewer brewer.pal

+#' @importFrom grDevices colorRampPalette

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

 #' @export

 setMethod("celdaProbabilityMap", signature(celdaMod = "celda_CG"),

@@ -129,6 +129,7 @@ normalizeCounts <- function(counts,

 #' @examples

 #' data(celdaCGMod)

 #' celdaModReorderedZ <- recodeClusterZ(celdaCGMod, c(1, 3), c(3, 1))

+#' @importFrom plyr mapvalues

 #' @export

 recodeClusterZ <- function(celdaMod, from, to) {

     if (length(setdiff(from, to)) != 0) {

@@ -254,6 +255,9 @@ compareCountMatrix <- function(counts,

 #' @return A vector of distinct colors that have been converted to HEX from HSV.

 #' @examples

 #' colorPal <- distinctColors(6) # can be used in plotting functions

+#' @importFrom grDevices colors

+#' @importFrom grDevices rgb2hsv

+#' @importFrom grDevices hsv

 #' @export

 distinctColors <- function(n,

     hues = c("red",

@@ -320,6 +324,7 @@ distinctColors <- function(n,

 # Wrapper function, creates checksum for matrix.

 # Feature names, cell names are not taken into account.

+#' @importFrom digest digest

 .createCountChecksum <- function(counts) {

     rownames(counts) <- NULL

     colnames(counts) <- NULL

@@ -378,6 +383,7 @@ distinctColors <- function(n,

 #' @examples

 #' data(celdaCGSim, celdaCGMod)

 #' featureModuleTable(celdaCGSim$counts, celdaCGMod, outputFile = NULL)

+#' @importFrom stringi stri_list2matrix

 #' @export

 featureModuleTable <- function(counts, celdaMod, outputFile = NULL) {

     factorize.matrix <- factorizeMatrix(counts, celdaMod)

@@ -421,6 +427,9 @@ featureModuleTable <- function(counts, celdaMod, outputFile = NULL) {

 #' data(celdaCGSim, celdaCGMod)

 #' violinPlot(counts = celdaCGSim$counts,

 #'     celdaMod = celdaCGMod, features = "Gene_1")

+#' @import ggplot2

+#' @importFrom grid unit

+#' @importFrom reshape2 melt

 #' @export

 violinPlot <- function(counts,

     celdaMod,

@@ -73,12 +73,9 @@

 #' plotHeatmap(celdaCGSim$counts,

 #'     z = clusters(celdaCGMod)$z, y = clusters(celdaCGMod)$y)

 #' @return list A list containing dendrogram information and the heatmap grob

-#' @import gtable

-#' @import grid

-#' @import scales

-#' @import RColorBrewer

-#' @import grDevices

 #' @import graphics

+#' @importFrom grid grid.newpage

+#' @importFrom grid grid.draw

 #' @export

 plotHeatmap <- function(counts,

     z = NULL,

@@ -118,6 +118,7 @@ simulateContaminatedMatrix <- function(C = 300,

 #  eta Numeric matrix. Rows represent features and columns represent cell

 # populations

 #  theta Numeric vector. Proportion of truely expressed transctripts

+#' @importFrom MCMCprecision fit_dirichlet

 .cDCalcEMDecontamination <- function(counts,

     phi,

     eta,

@@ -498,6 +499,7 @@ decontX <- function(counts,

 ## Make sure provided cell labels are the right type

+#' @importFrom plyr mapvalues

 .processCellLabels <- function(z, numCells) {

     if (length(z) != numCells) {

         stop("'z' must be of the same length as the number of cells in the",

@@ -26,9 +26,14 @@

 #' clusterDiffexpRes = differentialExpression(celdaCGSim$counts,

 #'     celdaCGMod, c1 = c(1, 2))

 #' @export

-#' @import data.table plyr

-#' @rawNamespace import(MAST, except = c(combine))

-#' @rawNamespace import(SummarizedExperiment, except = c(shift, rowRanges))

+#' @importFrom data.table as.data.table

+#' @importFrom MAST FromMatrix

+#' @importFrom MAST zlm

+#' @importFrom MAST summary

+#' @importFrom S4Vectors mcols

+#' @importFrom SummarizedExperiment assay

+#' @importFrom SummarizedExperiment colData

+#' @importFrom SummarizedExperiment assayNames

 differentialExpression <- function(counts,

     celdaMod,

     c1,

@@ -24,6 +24,7 @@

 #'     cm,

 #'     databases = c('GO_Biological_Process_2018','GO_Molecular_Function_2018'))

 #' @importFrom enrichR enrichr

+#' @importFrom enrichR listEnrichrDbs

 #' @export

 geneSetEnrich <- function(counts, celdaModel, databases, fdr = 0.05) {

     #check for correct celda object

@@ -34,6 +34,7 @@

 #'     colorLow = "grey",

 #'     colorMid = NULL,

 #'     colorHigh = "blue")

+#' @importFrom reshape2 melt

 #' @export

 plotDimReduceGrid <- function(dim1,

     dim2,

@@ -321,6 +322,7 @@ plotDimReduceModule <-

 #'     dim2 = celdaTsne[, 2],

 #'     cluster = as.factor(clusters(celdaCGMod)$z),

 #'     specificClusters = c(1, 2, 3))

+#' @importFrom ggrepel geom_text_repel

 #' @export

 plotDimReduceCluster <- function(dim1,

     dim2,

@@ -385,6 +387,7 @@ plotDimReduceCluster <- function(dim1,

 # dimensionality reduction with PCA before tSNE.

 # @param initialDims Integer.Number of dimensions from PCA to use as

 # input in tSNE.

+#' @importFrom Rtsne Rtsne

 .calculateTsne <- function(norm,

     perplexity = 20,

     maxIter = 2500,

@@ -409,6 +412,7 @@ plotDimReduceCluster <- function(dim1,

 # @param umapConfig An object of class umap.config,

 # containing configuration parameters to be passed to umap.

 # Default umap::umap.defualts.

+#' @importFrom umap umap

 .calculateUmap <- function(norm, umapConfig = umap::umap.defaults) {

     return(umap::umap(norm, umapConfig)$layout)

 }

@@ -1,7 +1,7 @@

 # Adapted originally from the very excellent pheatmap package

 # (https://cran.r-project.org/web/packages/pheatmap/index.html)

-

+#' @importFrom gtable gtable

 .lo <- function(rown,

     coln,

     nrow,

@@ -184,7 +184,7 @@

     }

     # Produce gtable

-    gt <- gtable(widths = unit.c(treeHeightRow,

+    gt <- gtable::gtable(widths = unit.c(treeHeightRow,

             annotRowWidth,

             matWidth,

             rownWidth,

@@ -582,6 +582,9 @@ vplayout <- function(x, y) {

     return(viewport(layout.pos.row = x, layout.pos.col = y))

 }

+#' @importFrom gtable gtable_height

+#' @importFrom gtable gtable_width

+#' @importFrom gtable gtable_add_grob

 .heatmapMotor <- function(matrix,

         borderColor,

         cellWidth,

@@ -643,12 +646,12 @@ vplayout <- function(x, y) {

         if (!is.na(fileName)) {

             if (is.na(height)) {

-                height <- convertHeight(gtable_height(res),

+                height <- convertHeight(gtable::gtable_height(res),

                     "inches",

                     valueOnly = TRUE)

             }

             if (is.na(width)) {

-                width <- convertWidth(gtable_width(res),

+                width <- convertWidth(gtable::gtable_width(res),

                     "inches",

                     valueOnly = TRUE)

             }

@@ -758,7 +761,7 @@ vplayout <- function(x, y) {

         # Draw title

         if (!is.na(main)) {

             elem <- .drawMain(main, fontSize = 1.3 * fontSize, ...)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 1,

                 l = 3,

@@ -769,7 +772,7 @@ vplayout <- function(x, y) {

         # Draw tree for the columns

         if (!.is.na2(treeCol) & treeHeightCol != 0) {

             elem <- .drawDendrogram(treeCol, gapsCol, horizontal = TRUE)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 2,

                 l = 3,

@@ -779,7 +782,7 @@ vplayout <- function(x, y) {

         # Draw tree for the rows

         if (!.is.na2(treeRow) & treeHeightRow != 0) {

             elem <- .drawDendrogram(treeRow, gapsRow, horizontal = FALSE)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 4,

                 l = 1,

@@ -795,7 +798,7 @@ vplayout <- function(x, y) {

             fontSizeNumber,

             numberColor)

-        res <- gtable_add_grob(res,

+        res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 4,

                 l = 3,

@@ -809,7 +812,7 @@ vplayout <- function(x, y) {

                 fontSize = fontSizeCol,

                 ...)

             elem <- do.call(.drawColnames, pars)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 5,

                 l = 3,

@@ -823,7 +826,7 @@ vplayout <- function(x, y) {

                 gaps = gapsRow,

                 fontSize = fontSizeRow, ...)

             elem <- do.call(.drawRownames, pars)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 4,

                 l = 4,

@@ -841,7 +844,7 @@ vplayout <- function(x, y) {

                 gapsCol,

                 fontSize,

                 horizontal = TRUE)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 3,

                 l = 3,

@@ -853,7 +856,7 @@ vplayout <- function(x, y) {

                 elem <- .drawAnnotationNames(annotationCol,

                     fontSize,

                     horizontal = TRUE)

-                res <- gtable_add_grob(res,

+                res <- gtable::gtable_add_grob(res,

                     elem,

                     t = 3,

                     l = 4,

@@ -872,7 +875,7 @@ vplayout <- function(x, y) {

                 gapsRow,

                 fontSize,

                 horizontal = FALSE)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = 4,

                 l = 2,

@@ -884,7 +887,7 @@ vplayout <- function(x, y) {

                 elem <- .drawAnnotationNames(annotationRow,

                     fontSize,

                     horizontal = FALSE)

-                res <- gtable_add_grob(res,

+                res <- gtable::gtable_add_grob(res,

                     elem,

                     t = 5,

                     l = 2,

@@ -907,7 +910,7 @@ vplayout <- function(x, y) {

                 ...)

             t <- ifelse(is.null(labelsRow), 4, 3)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                 elem,

                 t = t,

                 l = 6,

@@ -921,7 +924,7 @@ vplayout <- function(x, y) {

             elem <- .drawLegend(color, breaks, legend, fontSize = fontSize, ...)

             t <- ifelse(is.null(labelsRow), 4, 3)

-            res <- gtable_add_grob(res,

+            res <- gtable::gtable_add_grob(res,

                     elem,

                     t = t,

                     l = 5,

@@ -1213,6 +1216,7 @@ vplayout <- function(x, y) {

     return(mat)

 }

+#' @importFrom scales dscale

 .generateAnnotationColours <- function(annotation,

     annotationColors,

     drop) {

@@ -1493,6 +1497,7 @@ vplayout <- function(x, y) {

 #' }

 #'

 #' pheatmap(test, clusteringCallback = callback)

+#' @importFrom RColorBrewer brewer.pal

 semiPheatmap <- function(mat,

     color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),

     kmeansK = NA,

@@ -16,7 +16,7 @@ vignette: >

 In this vignette we will demonstrate how to use celda to perform cell and feature clustering with simulated data.

-#Installation

+# Installation

 celda can be installed from Bioconductor:

@@ -38,7 +38,7 @@ Complete list of help files are accessible using the help command with a `packag

 help(package = celda)

 ```

-To see the latest updates and releases or to post a bug, see our GitHub page at https://github.com/compbiomed/celda. To ask questions about running celda, visit our Google group at https://groups.google.com/forum/#!forum/celda-list. 

+To see the latest updates and releases or to post a bug, see our GitHub page at https://github.com/campbio/celda. To ask questions about running celda, post a thread on Bioconductor support site at https://support.bioconductor.org/.

 <br>

@@ -191,7 +191,8 @@ celdaProbabilityMap(counts = simCounts$counts, celdaMod = celdaModel)

 `moduleHeatmap` creates a heatmap using only the features from a specific feature module. Cells are ordered from those with the lowest probability of the module to the highest. If more than one module is used, then cells will be ordered by the probabilities of the first module.

 ```{r, eval = TRUE, fig.width = 7, fig.height = 7}

-moduleHeatmap(counts = simCounts$counts, celdaMod = celdaModel, featureModule = 1, topCells = 100)

+moduleHeatmap(counts = simCounts$counts, celdaMod = celdaModel,

+    featureModule = 1, topCells = 100)

 ```

 While `celdaHeatmap` will plot a heatmap directly with a celda object, the `plotHeatmap` function is a more general heatmap function which takes a normalized expression matrix as the input. Simple normalization of the counts matrix can be performed with the `normalizeCounts` function. For instance, if users want to display specific modules and cell populations, the `featureIx`  and `cells.ix` parameters can be used to select rows and columns out of the matrix. 

@@ -258,7 +259,7 @@ plotHeatmap(counts = normCounts[, clusters(celdaModel)$z %in% c(1, 2)],

 # Identifying the optimal number of cell subpopulations and feature modules

-In the previous example, the best K(the number of cell clusters) and L(the number of feature modules) was already known. However, the optimal K and L for each new dataset will likely not be known beforehand and multiple choices of K and L may need to be tried and compared. celda offers two sets of functions to determine the optimum K and L, `recursiveSplitModule`/`recursiveSplitCell`, and `celdaGridSearch`.

+In the previous example, the best K (the number of cell clusters) and L (the number of feature modules) was already known. However, the optimal K and L for each new dataset will likely not be known beforehand and multiple choices of K and L may need to be tried and compared. celda offers two sets of functions to determine the optimum K and L, `recursiveSplitModule`/`recursiveSplitCell`, and `celdaGridSearch`.

 ## recursiveSplitModule/recursiveSplitCell

@@ -271,7 +272,7 @@ moduleSplit <- recursiveSplitModule(counts = simCounts$counts,

     initialL = 2, maxL = 15)

 ```

-Perplexity is a statistical measure of how well a probability model can predict new data. Lower perplexity indicates a better model. The perplexity of each model can be visualized with `plotGridSearchPerplexity`. In general, visual inspection of the plot can be used to select the optimal number of modules ( L ) or cell populations ( K ) by identifying the "elbow" - where the rate of decrease in the perplexity starts to drop off.

+Perplexity is a statistical measure of how well a probability model can predict new data. Lower perplexity indicates a better model. The perplexity of each model can be visualized with `plotGridSearchPerplexity`. In general, visual inspection of the plot can be used to select the optimal number of modules (L) or cell populations (K) by identifying the "elbow" - where the rate of decrease in the perplexity starts to drop off.

 ```{r}

 plotGridSearchPerplexity(celdaList = moduleSplit)

@@ -389,6 +390,8 @@ The model prior to reordering cell labels compared to after reordering cell labe

 table(clusters(celdaModel)$z, clusters(celdaModelZRecoded)$z)

 ```

+# Session Information

+

 ```{r}

 sessionInfo()

 ```