diff --git a/R/plotting.R b/R/plotting.R
index 820446f..61d3b2b 100644
--- a/R/plotting.R
+++ b/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))+
diff --git a/bimodalR.Rproj b/bimodalR.Rproj
deleted file mode 100644
index cba1b6b..0000000
--- a/bimodalR.Rproj
+++ /dev/null
@@ -1,21 +0,0 @@
-Version: 1.0
-
-RestoreWorkspace: No
-SaveWorkspace: No
-AlwaysSaveHistory: Default
-
-EnableCodeIndexing: Yes
-UseSpacesForTab: Yes
-NumSpacesForTab: 2
-Encoding: UTF-8
-
-RnwWeave: Sweave
-LaTeX: pdfLaTeX
-
-AutoAppendNewline: Yes
-StripTrailingWhitespace: Yes
-
-BuildType: Package
-PackageUseDevtools: Yes
-PackageInstallArgs: --no-multiarch --with-keep.source
-PackageRoxygenize: rd,collate,namespace
diff --git a/vignettes/multimodalR.Rmd b/vignettes/multimodalR.Rmd
index 096b835..f58c09c 100644
--- a/vignettes/multimodalR.Rmd
+++ b/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)
 
 ```