@@ -111,7 +111,7 @@ pcaExplorer <- function(dds = NULL,

           "color_by",

           paste0("Select the group of samples to stratify the analysis. Can also assume multiple values"),

           "right", options = list(container = "body")),

-        numericInput("pca_nrgenes", label = "Nr of (most variant) genes:",

+        numericInput("pca_nrgenes", label = "Nr of (most variable) genes:",

                      value = 300, min = 50, max = 20000),

         shinyBS::bsTooltip(

           "pca_nrgenes", paste0(

@@ -128,7 +128,7 @@ the user can receive additional information on how to set the parameter, powered

 - **x-axis PC** - Select the principal component to display on the x axis

 - **y-axis PC** - Select the principal component to display on the y axis

 - **Group/color by** - Select the group of samples to stratify the analysis. Can also assume multiple values.

-- **Nr of (most variant) genes** - Number of genes to select for computing the principal components. The top n genes are

+- **Nr of (most variable) genes** - Number of genes to select for computing the principal components. The top n genes are

 selected ranked by their variance inter-samples

 - **Alpha** - Color transparency for the plots. Can assume values from 0 (transparent) to 1 (opaque)

 - **Labels size** - Size of the labels for the samples in the principal components plots

@@ -111,7 +111,7 @@ cat("Counts are ranging from", min(counts(values$mydds)),"to",max(counts(values$

 # PCA on the samples

-This plot shows how the samples are related to each other by plotting PC `r input$pc_x` vs PC `r input$pc_y`, using the top `r input$pca_nrgenes` most variant genes

+This plot shows how the samples are related to each other by plotting PC `r input$pc_x` vs PC `r input$pc_y`, using the top `r input$pca_nrgenes` most variable genes

 ```{r}

 res <- pcaplot(values$mydst,intgroup = input$color_by,ntop = input$pca_nrgenes,

@@ -166,7 +166,7 @@ By hovering over with the mouse, the user can receive additional information on

 - **x-axis PC** - Select the principal component to display on the x axis

 - **y-axis PC** - Select the principal component to display on the y axis

 - **Group/color by** - Select the group of samples to stratify the analysis. Can also assume multiple values.

-- **Nr of (most variant) genes** - Number of genes to select for computing the principal components. The top n genes are selected ranked by their variance inter-samples

+- **Nr of (most variable) genes** - Number of genes to select for computing the principal components. The top n genes are selected ranked by their variance inter-samples

 - **Alpha** - Color transparency for the plots. Can assume values from 0 (transparent) to 1 (opaque)

 - **Labels size** - Size of the labels for the samples in the principal components plots.

   This parameter also controls the size of the gene labels, which are displayed in the Genes View once the user has brushed an area in the main plot.