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

     SummarizedExperiment,

     MCMCprecision,

     ggrepel,

-    Rtsne

+    Rtsne,

+    S4Vectors

 Suggests:

     testthat,

     knitr,

@@ -77,40 +77,23 @@ 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(ggplot2)

 import(grDevices)

 import(graphics)

 import(grid)

+import(gridExtra)

+import(gridExtra, except = c(combine))

 import(gtable)

-import(stats)

-import(umap)

-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)

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

+import(plyr)

+import(scales)

 importFrom(enrichR,enrichr)

-importFrom(ggrepel,geom_text_repel)

-importFrom(gridExtra,grid.arrange)

-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)

 useDynLib(celda,"_colSumByGroup")

 useDynLib(celda,"_colSumByGroupChange")

 useDynLib(celda,"_colSumByGroup_numeric")

@@ -397,7 +397,7 @@ setGeneric("celdaHeatmap",

 #'

 #'

 logLikelihood <- function(counts, model, ...) {

-    do.call(paste0("logLikelihood.", model),

+    do.call(paste0("logLikelihood", model),

         args = list(counts = counts, ...))

 }

@@ -461,7 +461,7 @@ setGeneric("perplexity",

 #' dim(celdaCGSim$counts)

 #' @export

 simulateCells <- function(model, ...) {

-    do.call(paste0("simulateCells.", model), args = list(...))

+    do.call(paste0("simulateCells", model), args = list(...))

 }

@@ -522,7 +522,7 @@ celda_C <- function(counts,

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

 #' simCounts <- celdaCSim$counts

 #' @export

-simulateCells.celda_C <- function(model,

+simulateCellscelda_C <- function(model,

     S = 5,

     CRange = c(50, 100),

     NRange = c(500, 1000),

@@ -726,7 +726,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_C"),

 #' @seealso `celda_C()` for clustering cells

 #' @examples

 #' data(celdaCSim)

-#' loglik <- logLikelihood.celda_C(celdaCSim$counts,

+#' loglik <- logLikelihoodcelda_C(celdaCSim$counts,

 #'     sampleLabel = celdaCSim$sampleLabel,

 #'     z = celdaCSim$z,

 #'     K = celdaCSim$K,

@@ -741,7 +741,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_C"),

 #'     alpha = celdaCSim$alpha,

 #'     beta = celdaCSim$beta)

 #' @export

-logLikelihood.celda_C <- function(counts, sampleLabel, z, K, alpha, beta) {

+logLikelihoodcelda_C <- function(counts, sampleLabel, z, K, alpha, beta) {

     if (sum(z > K) > 0) {

         stop("An entry in z contains a value greater than the provided K.")

@@ -584,7 +584,7 @@ celda_CG <- function(counts,

 #' @examples

 #' celdaCGSim <- simulateCells(model = "celda_CG")

 #' @export

-simulateCells.celda_CG <- function(model,

+simulateCellscelda_CG <- function(model,

     S = 5,

     CRange = c(50, 100),

     NRange = c(500, 1000),

@@ -912,7 +912,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_CG"),

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

 #' @examples

 #' data(celdaCGSim)

-#' loglik <- logLikelihood.celda_CG(celdaCGSim$counts,

+#' loglik <- logLikelihoodcelda_CG(celdaCGSim$counts,

 #'     sampleLabel = celdaCGSim$sampleLabel,

 #'     z = celdaCGSim$z,

 #'     y = celdaCGSim$y,

@@ -935,7 +935,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_CG"),

 #'     gamma = celdaCGSim$gamma,

 #'     delta = celdaCGSim$delta)

 #' @export

-logLikelihood.celda_CG <- function(counts,

+logLikelihoodcelda_CG <- function(counts,

     sampleLabel,

     z,

     y,

@@ -454,7 +454,7 @@ celda_G <- function(counts,

 #' @examples

 #' celdaGSim <- simulateCells(model = "celda_G")

 #' @export

-simulateCells.celda_G <- function(model,

+simulateCellscelda_G <- function(model,

     C = 100,

     NRange = c(500, 1000),

     G = 100,

@@ -696,7 +696,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_G"),

 #' @seealso `celda_G()` for clustering features

 #' @examples

 #' data(celdaGSim)

-#' loglik <- logLikelihood.celda_G(celdaGSim$counts,

+#' loglik <- logLikelihoodcelda_G(celdaGSim$counts,

 #'     y = celdaGSim$y,

 #'     L = celdaGSim$L,

 #'     beta = celdaGSim$beta,

@@ -711,7 +711,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_G"),

 #'     delta = celdaGSim$delta,

 #'     gamma = celdaGSim$gamma)

 #' @export

-logLikelihood.celda_G <- function(counts, y, L, beta, delta, gamma) {

+logLikelihoodcelda_G <- function(counts, y, L, beta, delta, gamma) {

     if (sum(y > L) > 0) {

         stop("An entry in y contains a value greater than the provided L.")

     }

@@ -65,7 +65,7 @@ resamplePerplexity <- function(counts,

 #' plotGridSearchPerplexity(celdaCGGridSearchRes)

 #' @export

 plotGridSearchPerplexity <- function(celdaList, sep = 1) {

-    do.call(paste0("plotGridSearchPerplexity.",

+    do.call(paste0("plotGridSearchPerplexity",

         as.character(class(celdaList@resList[[1]]))),

         args = list(celdaList, sep))

 }

@@ -86,7 +86,7 @@ plotGridSearchPerplexity <- function(celdaList, sep = 1) {

 #' )

 #' plotGridSearchPerplexity(celdaCGGridSearchRes)

 #' @export

-plotGridSearchPerplexity.celda_CG <- function(celdaList, sep) {

+plotGridSearchPerplexitycelda_CG <- function(celdaList, sep) {

     if (!all(c("K", "L") %in% colnames(celdaList@runParams))) {

         stop("celdaList@runParams needs K and L columns.")

     }

@@ -158,7 +158,7 @@ plotGridSearchPerplexity.celda_CG <- function(celdaList, sep) {

 #' )

 #' plotGridSearchPerplexity(celdaCGGridSearchRes)

 #' @export

-plotGridSearchPerplexity.celda_C <- function(celdaList, sep) {

+plotGridSearchPerplexitycelda_C <- function(celdaList, sep) {

     if (!all(c("K") %in% colnames(celdaList@runParams))) {

         stop("runParams(celdaList) needs the column K.")

     }

@@ -207,7 +207,7 @@ plotGridSearchPerplexity.celda_C <- function(celdaList, sep) {

 #'   celdaCGGridSearchRes)

 #' plotGridSearchPerplexity(celdaCGGridSearchRes)

 #' @export

-plotGridSearchPerplexity.celda_G <- function(celdaList, sep) {

+plotGridSearchPerplexitycelda_G <- function(celdaList, sep) {

     if (!all(c("L") %in% colnames(celdaList@runParams))) {

         stop("celdaList@runParams needs the column L.")

     }

@@ -23,7 +23,7 @@

             ix <- z == i

             newZ <- z

             newZ[ix] <- ifelse(clustLabel@clusters$z == 2, i, K)

-            ll <- logLikelihood.celda_C(counts, s, newZ, K, alpha, beta)

+            ll <- logLikelihoodcelda_C(counts, s, newZ, K, alpha, beta)

             if (ll > bestLl) {

                 bestZ <- newZ

@@ -61,7 +61,7 @@

             ix <- y == i

             newY <- y

             newY[ix] <- ifelse(clustLabel@clusters$y == 2, i, L)

-            ll <- logLikelihood.celda_G(counts, newY, L, beta, delta, gamma)

+            ll <- logLikelihoodcelda_G(counts, newY, L, beta, delta, gamma)

             if (ll > bestLl) {

                 bestY <- newY

@@ -265,7 +265,7 @@ recursiveSplitCell <- function(counts,

                 overallZ <- tempModel@clusters$z

             } else {

                 overallZ <- tempSplit$z

-                ll <- logLikelihood.celda_CG(counts,

+                ll <- logLikelihoodcelda_CG(counts,

                     s,

                     overallZ,

                     tempModel@clusters$y,

@@ -353,7 +353,7 @@ recursiveSplitCell <- function(counts,

             reorder = reorder)

         currentK <- length(unique(modelInitial@clusters$z)) + 1

         overallZ <- modelInitial@clusters$z

-        ll <- logLikelihood.celda_C(counts, s, overallZ, currentK,

+        ll <- logLikelihoodcelda_C(counts, s, overallZ, currentK,

             alpha, beta)

         modelInitial@params$countChecksum <- countChecksum

         modelInitial@completeLogLik <- ll

@@ -391,7 +391,7 @@ recursiveSplitCell <- function(counts,

             }

             # Need to change below line to use decompose counts to save time

-            ll <- logLikelihood.celda_C(counts, s, overallZ, currentK,

+            ll <- logLikelihoodcelda_C(counts, s, overallZ, currentK,

                 alpha, beta)

             tempModel <- methods::new("celda_C",

                 clusters = list(z = overallZ),

@@ -471,7 +471,7 @@ recursiveSplitCell <- function(counts,

             } else {

                 overallZ <- tempSplit$z

                 ll <-

-                    logLikelihood.celda_C(counts, s, overallZ,

+                    logLikelihoodcelda_C(counts, s, overallZ,

                         currentK, alpha, beta)

                 tempModel <- methods::new("celda_C",

                     clusters = list(z = overallZ),

@@ -43,7 +43,6 @@ Uses MAST to find differentially expressed features for

 }

 \examples{

 data(celdaCGSim, celdaCGMod)

-library(SummarizedExperiment)

 clusterDiffexpRes = differentialExpression(celdaCGSim$counts,

     celdaCGMod, c1 = c(1, 2))

 }