diff --git a/R/ElectrodeBurstsClass.R b/R/ElectrodeBurstsClass.R
index 5f51923..93b8ab4 100644
--- a/R/ElectrodeBurstsClass.R
+++ b/R/ElectrodeBurstsClass.R
@@ -202,7 +202,7 @@ ElectrodeBursts <- R6Class(
     #' @param plot_title The title of the plot.
     #' @return A ggpubr object representing the combined raster plots.
     #'
-    create_comparison_raster_plot = function(control_group, treatments_array, plot_title) {
+    create_comparison_raster_plot = function(control_group, treatments_array, plot_title, well.num) {
       create_raster <- function(well, x_lim) {
         plot_data <- self$data %>%
           dplyr::mutate(
@@ -296,7 +296,7 @@ ElectrodeBursts <- R6Class(
         # Combine the two well plots side by side
         combined_plot <- ggpubr::ggarrange(
           plotlist = well_plots,
-          ncol = 2,
+          ncol = well.num,
           nrow = 1,
           common.legend = TRUE,
           legend = "none"
diff --git a/README.md b/README.md
index cc3d96f..139a9f3 100644
--- a/README.md
+++ b/README.md
@@ -78,6 +78,7 @@ Key methods:
      - `control_group`: character - The control group to be included in the comparison
      - `treatments_array`: character vector - An array of treatment names to compare
      - `plot_title`: character - The title of the plot
+     - `num.well`: number - Highest number of well replicates amongst chosen conditions
    - Returns: ggpubr object - A combined raster plot comparing multiple treatments
 
 ## Usage
@@ -86,12 +87,14 @@ To use this package, start by initializing the appropriate class with your data
 
 ```R
 # For single file analysis
+# file is the average value .csv, the one with no subtitles
 mea_analysis <- MEAnalysis$new("path/to/your/file.csv")
 
 # For batch analysis
 batch_analysis <- BatchMEAnalysis$new(list_of_file_paths)
 
 # For electrode burst analysis
+# file is the electrode_burst_list
 electrode_bursts <- ElectrodeBursts$new("path/to/your/file.csv")
 ```
 
@@ -104,7 +107,7 @@ Then, you can use the various methods provided by each class to analyze your dat
 # Create a treatment averages t-test plot
 plot <- mea_analysis$treatment_averages_t_test_plot(
   "Control",
-  c("Treatment1", "Treatment2"),
+  c("Control", "Treatment1", "Treatment2"),
   "Treatment Averages T-Test Plot"
 )
 # plot is a ggplot object
@@ -126,7 +129,7 @@ plot <- batch_analysis$run_mea_analysis(
 # Generate significance overview
 overview <- batch_analysis$generate_significance_overview(
   "Control",
-  c("Treatment1", "Treatment2"),
+  c("Control", "Treatment1", "Treatment2"),
   c("MeanFiringRate", "BurstFrequency"),
   "Significance Overview"
 )
@@ -139,8 +142,9 @@ overview <- batch_analysis$generate_significance_overview(
 # Create a comparison raster plot
 plot <- electrode_bursts$create_comparison_raster_plot(
   "Control",
-  c("Treatment1", "Treatment2"),
-  "Comparison Raster Plot"
+  c("Control", "Treatment1", "Treatment2"),
+  "Comparison Raster Plot",
+  3
 )
 # plot is a ggpubr object
 ```