@@ -519,7 +519,7 @@ setMethod("resamplePerplexity",

 #'  parameters.

 #' @export

 setGeneric("plotGridSearchPerplexity",

-    function(x, altExpName = "featureSubset", sep = 1, alpha = 0.5) {

+    function(x, altExpName = "featureSubset", sep = 5, alpha = 0.5) {

     standardGeneric("plotGridSearchPerplexity")})

@@ -531,7 +531,7 @@ setGeneric("plotGridSearchPerplexity",

 #' @export

 setMethod("plotGridSearchPerplexity",

     signature(x = "SingleCellExperiment"),

-    function(x, altExpName = "featureSubset", sep = 1, alpha = 0.5) {

+    function(x, altExpName = "featureSubset", sep = 5, alpha = 0.5) {

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         celdaList <- S4Vectors::metadata(altExp)$celda_grid_search

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

@@ -553,7 +553,7 @@ setMethod("plotGridSearchPerplexity",

 #' @export

 setMethod("plotGridSearchPerplexity",

     signature(x = "celdaList"),

-    function(x, sep = 1, alpha = 0.5) {

+    function(x, sep = 5, alpha = 0.5) {

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

             as.character(class(resList(x)[[1]]))),

             args = list(x, sep, alpha))

@@ -777,8 +777,7 @@ setMethod("plotGridSearchPerplexity",

 #' @title Visualize perplexity differences of a list of celda models

 #' @description Visualize perplexity differences of every model in a celdaList,

-#'  by unique K/L combinations. Line represents centered moving average with

-#'  windows of length \code{n}.

+#'  by unique K/L combinations. 

 #' @param x Can be one of

 #' \itemize{

 #'  \item A \linkS4class{SingleCellExperiment} object returned from

@@ -789,15 +788,14 @@ setMethod("plotGridSearchPerplexity",

 #' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param sep Numeric. Breaks in the x axis of the resulting plot.

-#' @param n Integer. Width of the rolling window. Default 10.

 #' @param alpha Numeric. Passed to \link{geom_jitter}. Opacity of the points.

 #'  Values of alpha range from 0 to 1, with lower values corresponding

 #'  to more transparent colors.

-#' @return A ggplot plot object showing perplexity diferences as a function of

+#' @return A ggplot plot object showing perplexity differences as a function of

 #'  clustering parameters.

 #' @export

 setGeneric("plotRPC",

-    function(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5) {

+    function(x, altExpName = "featureSubset", sep = 5, alpha = 0.5) {

     standardGeneric("plotRPC")})

@@ -805,24 +803,24 @@ setGeneric("plotRPC",

 #' @examples

 #' data(sceCeldaCGGridSearch)

 #' sce <- resamplePerplexity(sceCeldaCGGridSearch)

-#' plotRPC(sce, n = 1)

+#' plotRPC(sce)

 #' @export

 setMethod("plotRPC",

     signature(x = "SingleCellExperiment"),

-    function(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5) {

+    function(x, altExpName = "featureSubset", sep = 5, alpha = 0.5) {

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         model <- altExp@metadata$celda_grid_search@celdaGridSearchParameters$

             model

         celdaList <- S4Vectors::metadata(altExp)$celda_grid_search

         if (model == "celda_C") {

-            g <- .plotRPCC(celdaList, sep, n = n,

+            g <- .plotRPCC(celdaList, sep, 

                 alpha = alpha)

         } else if (model == "celda_G") {

-            g <- .plotRPCG(celdaList, sep, n = n,

+            g <- .plotRPCG(celdaList, sep, 

                 alpha = alpha)

         } else if (model == "celda_CG") {

-            g <- .plotRPCCG(celdaList, sep, n = n,

+            g <- .plotRPCCG(celdaList, sep, 

                 alpha = alpha)

         } else {

             stop("S4Vectors::metadata(altExp(x, altExpName))$",

@@ -842,20 +840,20 @@ setMethod("plotRPC",

 #' celdaCGGridSearchRes <- resamplePerplexity(

 #'   celdaCGSim$counts,

 #'   celdaCGGridSearchRes)

-#' plotRPC(celdaCGGridSearchRes, n = 1)

+#' plotRPC(celdaCGGridSearchRes)

 #' @export

 setMethod("plotRPC",

     signature(x = "celdaList"),

-    function(x, sep = 1, n = 10, alpha = 0.5) {

+    function(x, sep = 5, alpha = 0.5) {

         g <- do.call(paste0(".plotRPC",

             unlist(strsplit(as.character(class(resList(x)[[1]])), "_"))[[2]]),

-            args = list(x, sep, n, alpha))

+            args = list(x, sep, alpha))

         return(g)

     }

 )

-.plotRPCCG <- function(celdaList, sep, n, alpha) {

+.plotRPCCG <- function(celdaList, sep, alpha) {

     # fix check note

     K <- L <- perpdiffK <- meanperpdiffK <- perpdiffL <- meanperpdiffL <- NULL

@@ -895,10 +893,9 @@ setMethod("plotRPC",

         colnames(diffMeansByK) <- c("K", "L", "meanperpdiffK")

         diffMeansByK$K <- as.factor(diffMeansByK$K)

         diffMeansByK$L <- as.factor(diffMeansByK$L)

-        diffMeansByK$rollmean <- data.table::frollmean(

-            diffMeansByK$meanperpdiffK, n = n, align = "center")

         diffMeansByK <- diffMeansByK[stats::complete.cases(diffMeansByK), ]

-

+        diffMeansByK$spline <- stats::smooth.spline(diffMeansByK$meanperpdiffK)$y

+        

         if (nlevels(dt$L) > 1) {

             plot <- ggplot2::ggplot(dt[!is.na(perpdiffK), ],

                 ggplot2::aes_string(x = "K",

@@ -907,7 +904,7 @@ setMethod("plotRPC",

                     ggplot2::aes_string(color = "L")) +

                 ggplot2::scale_color_discrete(name = "L") +

                 ggplot2::geom_path(data = diffMeansByK,

-                    ggplot2::aes_string(x = "K", y = "rollmean", group = "L",

+                    ggplot2::aes_string(x = "K", y = "spline", group = "L",

                         color = "L"), size = 1) +

                 ggplot2::ylab("Rate of perplexity change") +

                 ggplot2::xlab("K") +

@@ -925,7 +922,7 @@ setMethod("plotRPC",

                     color = "grey", alpha = alpha) +

                 ggplot2::scale_color_manual(name = "L", values = "black") +

                 ggplot2::geom_path(data = diffMeansByK,

-                    ggplot2::aes_string(x = "K", y = "rollmean", group = "L",

+                    ggplot2::aes_string(x = "K", y = "spline", group = "L",

                         color = "L"), size = 1) +

                 ggplot2::ylab("Rate of perplexity change") +

                 ggplot2::xlab("K") +

@@ -952,10 +949,9 @@ setMethod("plotRPC",

         colnames(diffMeansByL) <- c("K", "L", "meanperpdiffL")

         diffMeansByL$K <- as.factor(diffMeansByL$K)

         diffMeansByL$L <- as.factor(diffMeansByL$L)

-        diffMeansByL$rollmean <- data.table::frollmean(

-            diffMeansByL$meanperpdiffL, n = n, align = "center")

         diffMeansByL <- diffMeansByL[stats::complete.cases(diffMeansByL), ]

-

+        diffMeansByL$spline <- stats::smooth.spline(diffMeansByL$meanperpdiffL)$y

+        

         plot <- ggplot2::ggplot(dt[!is.na(perpdiffL), ],

             ggplot2::aes_string(x = "L", y = "perpdiffL")) +

             ggplot2::geom_jitter(height = 0, width = 0.1,

@@ -964,7 +960,7 @@ setMethod("plotRPC",

             ggplot2::geom_path(

                 data = diffMeansByL,

                 ggplot2::aes_string(

-                    x = "L", y = "rollmean", group = "K", color = "K"),

+                    x = "L", y = "spline", group = "K", color = "K"),

                 size = 1) +

             ggplot2::ylab("Rate of perplexity change") +

             ggplot2::xlab("L") +

@@ -983,7 +979,7 @@ setMethod("plotRPC",

 }

-.plotRPCC <- function(celdaList, sep, n, alpha) {

+.plotRPCC <- function(celdaList, sep, alpha) {

     K <- perpdiffK <- meanperpdiffK <- NULL # fix check note

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

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

@@ -1015,17 +1011,16 @@ setMethod("plotRPC",

             FUN = mean))

         colnames(diffMeansByK) <- c("K", "meanperpdiffK")

         diffMeansByK$K <- as.factor(diffMeansByK$K)

-        diffMeansByK$rollmean <- data.table::frollmean(

-            diffMeansByK$meanperpdiffK, n = n, align = "center")

         diffMeansByK <- diffMeansByK[stats::complete.cases(diffMeansByK), ]

-

+        diffMeansByK$spline <- stats::smooth.spline(diffMeansByK$meanperpdiffK)$y

+        

         plot <- ggplot2::ggplot(dt[!is.na(perpdiffK), ],

             ggplot2::aes_string(x = "K",

                 y = "perpdiffK")) +

             ggplot2::geom_jitter(height = 0, width = 0.1,

                 color = "grey", alpha = alpha) +

             ggplot2::geom_path(data = diffMeansByK,

-                ggplot2::aes_string(x = "K", y = "rollmean", group = 1),

+                ggplot2::aes_string(x = "K", y = "spline", group = 1),

                 size = 1) +

             ggplot2::ylab("Perplexity difference compared to previous K") +

             ggplot2::xlab("K") +

@@ -1043,7 +1038,7 @@ setMethod("plotRPC",

 }

-.plotRPCG <- function(celdaList, sep, n, alpha) {

+.plotRPCG <- function(celdaList, sep, alpha) {

     L <- perpdiffL <- meanperpdiffL <- NULL # fix check note

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

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

@@ -1075,17 +1070,16 @@ setMethod("plotRPC",

             FUN = mean))

         colnames(diffMeansByL) <- c("L", "meanperpdiffL")

         diffMeansByL$L <- as.factor(diffMeansByL$L)

-        diffMeansByL$rollmean <- data.table::frollmean(

-            diffMeansByL$meanperpdiffL, n = n, align = "center")

         diffMeansByL <- diffMeansByL[stats::complete.cases(diffMeansByL), ]

-

+        diffMeansByL$spline <- stats::smooth.spline(diffMeansByL$meanperpdiffL)$y

+        

         plot <- ggplot2::ggplot(dt[!is.na(perpdiffL), ],

             ggplot2::aes_string(x = "L",

                 y = "perpdiffL")) +

             ggplot2::geom_jitter(height = 0, width = 0.1,

                 color = "grey", alpha = alpha) +

             ggplot2::geom_path(data = diffMeansByL,

-                ggplot2::aes_string(x = "L", y = "rollmean", group = 1),

+                ggplot2::aes_string(x = "L", y = "spline", group = 1),

                 size = 1) +

             ggplot2::ylab("Perplexity difference compared to previous L") +

             ggplot2::xlab("L") +

@@ -6,11 +6,11 @@

 \alias{plotGridSearchPerplexity,celdaList-method}

 \title{Visualize perplexity of a list of celda models}

 \usage{

-plotGridSearchPerplexity(x, altExpName = "featureSubset", sep = 1, alpha = 0.5)

+plotGridSearchPerplexity(x, altExpName = "featureSubset", sep = 5, alpha = 0.5)

-\S4method{plotGridSearchPerplexity}{SingleCellExperiment}(x, altExpName = "featureSubset", sep = 1, alpha = 0.5)

+\S4method{plotGridSearchPerplexity}{SingleCellExperiment}(x, altExpName = "featureSubset", sep = 5, alpha = 0.5)

-\S4method{plotGridSearchPerplexity}{celdaList}(x, sep = 1, alpha = 0.5)

+\S4method{plotGridSearchPerplexity}{celdaList}(x, sep = 5, alpha = 0.5)

 }

 \arguments{

 \item{x}{Can be one of

@@ -6,11 +6,11 @@

 \alias{plotRPC,celdaList-method}

 \title{Visualize perplexity differences of a list of celda models}

 \usage{

-plotRPC(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5)

+plotRPC(x, altExpName = "featureSubset", sep = 5, alpha = 0.5)

-\S4method{plotRPC}{SingleCellExperiment}(x, altExpName = "featureSubset", sep = 1, n = 10, alpha = 0.5)

+\S4method{plotRPC}{SingleCellExperiment}(x, altExpName = "featureSubset", sep = 5, alpha = 0.5)

-\S4method{plotRPC}{celdaList}(x, sep = 1, n = 10, alpha = 0.5)

+\S4method{plotRPC}{celdaList}(x, sep = 5, alpha = 0.5)

 }

 \arguments{

 \item{x}{Can be one of

@@ -26,29 +26,26 @@ to use. Default "featureSubset".}

 \item{sep}{Numeric. Breaks in the x axis of the resulting plot.}

-\item{n}{Integer. Width of the rolling window. Default 10.}

-

 \item{alpha}{Numeric. Passed to \link{geom_jitter}. Opacity of the points.

 Values of alpha range from 0 to 1, with lower values corresponding

 to more transparent colors.}

 }

 \value{

-A ggplot plot object showing perplexity diferences as a function of

+A ggplot plot object showing perplexity differences as a function of

  clustering parameters.

 }

 \description{

 Visualize perplexity differences of every model in a celdaList,

- by unique K/L combinations. Line represents centered moving average with

- windows of length \code{n}.

+ by unique K/L combinations.

 }

 \examples{

 data(sceCeldaCGGridSearch)

 sce <- resamplePerplexity(sceCeldaCGGridSearch)

-plotRPC(sce, n = 1)

+plotRPC(sce)

 data(celdaCGSim, celdaCGGridSearchRes)

 ## Run various combinations of parameters with 'celdaGridSearch'

 celdaCGGridSearchRes <- resamplePerplexity(

   celdaCGSim$counts,

   celdaCGGridSearchRes)

-plotRPC(celdaCGGridSearchRes, n = 1)

+plotRPC(celdaCGGridSearchRes)

 }