SmallEdits

Updated mistake in vignette
plotClassification was plotting stacks before, now bars are placed next to each other

R/plotting.R

@@ -192,7 +192,7 @@ plotClassifications <- function(classifications, name="classifications"){
     nameClassifications=c("FN","FP","TN","TP")
     value <- unname(unlist(classificationsPerc[i,]))
     data <- data.frame(nameClassifications,value)
-    classPlot <- ggplot2::ggplot(data = data,ggplot2::aes(x=name,y=value))+
+    classPlot <- ggplot2::ggplot(data = data,ggplot2::aes(x=nameClassifications,y=value))+
       ggplot2::geom_bar(stat = "identity",fill= c("red4","firebrick2","limegreen","lawngreen"))+
       ggplot2::scale_fill_manual(values = c("red4","firebrick2","limegreen","lawngreen"))+
       ggplot2::theme_bw()+ggplot2::theme(legend.position = "none",plot.title = ggplot2::element_text(hjust = 0.5))+

vignettes/multimodalR.Rmd

@@ -644,7 +644,7 @@ If all of those requirements are fulfilled, the function should return 'TRUE', i
 ## Usage of the algorithms
 
 In this package three algorithms that can be used for detecting multimodality, 
-are implemented.
+are implemented. The mclust algorithm that can be used with useMclust() is the one recommended to detect multimodal genes in gene expression data sets.
 
 ### mclust
 Mclust uses a finite Gaussian mixture modeling fitted via EM algorithm for 
@@ -754,7 +754,7 @@ To use the FlexMix algorithm on your expression data frame (simulated or not)
 and gain information which genes are bimodal use the useFlexmix() command:
 ```{r useFlexmix,eval = FALSE}
 #use FlexMix with the expression data frame expression
-flexmixOutput <- useFlexmix(expression = expression,maxModality = 2,reps = 1)
+flexmixOutput <- useFlexmix(expression = expression,maxModality = 2)
 
 ```