clarify parameter options in description

R/Plot.R

@@ -330,8 +330,8 @@ plotSpike <- function(estSpike, annot=TRUE) {
                   Deviation=sd(normCounts),
                   Error=sd(normCounts)/sqrt(n())) %>%
     dplyr::ungroup()
-  limits <- ggplot2::aes(ymax = log10(Expectation) + log10(Error),
-                         ymin= log10(Expectation) - log10(Error))
+  limits <- ggplot2::aes(ymax = Expectation + Error,
+                         ymin= Expectation - Error)
   Calibration = cal.dat %>%
     dplyr::left_join(estSpike$Input$spikeInfo, by="SpikeID") %>%
     dplyr::group_by(SampleID) %>%
@@ -342,16 +342,16 @@ plotSpike <- function(estSpike, annot=TRUE) {
 
   # calibration curve plot
   cal.plot <- ggplot2::ggplot(data = cal.info.dat,
-                              ggplot2::aes(x=log10(SpikeInput),
-                                           y=log10(Expectation))) +
-    ggplot2::geom_pointrange(limits) +
+                              ggplot2::aes(x=SpikeInput,
+                                           y=Expectation)) +
+    # ggplot2::geom_pointrange(limits) +
     ggplot2::geom_point() +
-    ggplot2::geom_smooth(method='lm',formula=y~x) +
+    ggplot2::geom_smooth(method='lm', se=TRUE) +
     ggplot2::annotate("text", label = paste0("italic(R) ^ 2 == ",
                                              round(Calibration$Rsquared, digits = 2),
                                              "%+-%",
                                              round(Calibration$RsquaredSE, digits = 2)),
-                      parse = T, x = 0.2, y = 4, size = 4) +
+                      parse = T, x = 0.1, y = 100, size = 4) +
     ggplot2::theme_minimal() +
     ggplot2::scale_x_log10(labels=c("0.1","1","10","100","1,000"),breaks=c(0.1,1,10,100,1000)) +
     ggplot2::scale_y_log10(labels=c("0.1","1","10","100","1,000"),breaks=c(0.1,1,10,100,1000)) +
@@ -372,7 +372,7 @@ plotSpike <- function(estSpike, annot=TRUE) {
     dplyr::left_join(estSpike$Input$spikeInfo, by="SpikeID")
 
   capture.plot <- ggplot2::ggplot(data = capture.dat,
-                                  ggplot2::aes(x=log10(SpikeInput+1),
+                                  ggplot2::aes(x=SpikeInput,
                                                y=hat_p_success)) +
     ggplot2::geom_point() +
     ggplot2::geom_smooth(method="glm",  method.args = list(family = "binomial"), se=T) +
@@ -551,7 +551,7 @@ plotCounts <- function(simCounts, Distance, Scale, DimReduce, verbose = T) {
 # plotEvalDE -------------------------------------------------------------
 #' @name plotEvalDE
 #' @aliases plotEvalDE
-#' @title Visualize power assessment
+#' @title Visualize statistical power assessment
 #' @description This function plots the results of \code{\link{evaluateDE}} for assessing the error rates and sample size requirements.
 #' @usage plotEvalDE(evalRes, rate=c('marginal', 'stratified'),
 #'                    quick=TRUE, annot=TRUE)
@@ -824,7 +824,7 @@ plotEvalDE <- function(evalRes, rate=c('marginal', 'stratified'), quick=TRUE, an
 
 #' @name plotEvalSim
 #' @aliases plotEvalSim
-#' @title Visualize power assessment
+#' @title Visualize simulation setup assessment
 #' @description This function plots the results of \code{\link{evaluateSim}} for assessing the setup performance, i.e. normalisation method performance.
 #' @usage plotEvalSim(evalRes, annot=TRUE)
 #' @param evalRes The output of \code{\link{evaluateSim}}.
@@ -1093,7 +1093,7 @@ plotEvalSim <- function(evalRes, annot=TRUE) {
 
 #' @name plotTime
 #' @aliases plotTime
-#' @title Visualize computational time
+#' @title Visualize computational time of simulations
 #' @description This function plots the computational running time of simulations.
 #' @usage plotTime(simRes, Table=TRUE, annot=TRUE)
 #' @param simRes The output of \code{\link{simulateDE}}.

R/Simulate.R

@@ -8,7 +8,7 @@
 #' The simulation parameters are specified with \code{\link{SimSetup}}.
 #' The user needs to specify furthermore
 #' the number of samples per group, preprocessing, normalisation and differential testing method.
-#' There is also the option to consider spike-ins. \cr
+#' There is also the option to consider spike-ins or apply imputation prior to normalisation. \cr
 #' The return object contains DE test results from all simulations as well as descriptive statistics.
 #' The error matrix calculations will be conducted with \code{\link{evaluateDE}}.\cr
 #' @usage simulateDE(n1=c(20,50,100), n2=c(30,60,120),
@@ -24,20 +24,25 @@
 #' @param n1,n2 Integer vectors specifying the number of biological replicates in each group. Default values are n1=c(20,50,100) and n2=c(30,60,120).
 #' @param sim.settings This object specifies the simulation setup. This must be the return object from \code{\link{SimSetup}}.
 #' @param DEmethod A character vector specifying the DE detection method to be used.
-#' Please consult the Details section for available options.
+#' Available options include "edgeR-LRT", "edgeR-QL", "edgeR-zingeR", "edgeR-ZINB-WaVE", "limma-voom", "limma-trend", "DESeq2", "DESeq2-zingeR", "DESeq2-ZINB-WaVE", "ROTS", "baySeq", "NOISeq", "EBSeq", "MAST", "scde", "BPSC", "scDD", "DECENT".
+#' Please consult the Details section for more information.
 #' @param normalisation Normalisation method to use.
-#' Please consult the Details section for available options.
+#' Available options include 'TMM', 'MR', 'PosCounts', 'UQ', 'scran', 'Linnorm',
+#' 'SCnorm', 'Census'.
+#' Please consult the Details section for more information.
 #' @param Preclust Whether to run a  hierarchical clustering prior to normalisation.
 #' This is implemented for scran only. Default is \code{FALSE}.
 #' For details, see \code{\link[scran]{quickCluster}}.
 #' @param Prefilter A character vector specifying the gene expression filtering method
 #' to be used prior to normalisation (and possibly imputation).
 #' Default is \code{NULL}, i.e. no filtering.
-#' Please consult the Details section for available options.
+#' Available options include 'FreqFilter', 'CountFilter'.
+#' Please consult the Details section for more information.
 #' @param Impute A character vector specifying the gene expression imputation method
 #' to be used prior to normalisation.
 #' Default is \code{NULL}, i.e. no imputation.
-#' Please consult the Details section for available options.
+#' Available options include 'DrImpute', 'scImpute', 'scone', 'Seurat', 'SAVER'.
+#' Please consult the Details section for more information.
 #' @param spikeIns Logical value to indicate whether to simulate spike-ins.
 #' Default is \code{FALSE}.
 #' @param NCores integer positive number of cores for parallel processing.