Correlation Graph

library(ggpubr)
heteromanova<-read.table("matrix_containing_numericv_values.txt", header=T, sep="\t", na.strings = "NA", fill=T)
heteromanovanum <- heteromanova[, 2:dimensions]

# Basic plots
ggscatter(heteromanova, x = "Drug", y = "DEPTHscore",
          add = "reg.line",                                 # Add regression line
          conf.int = TRUE,                                  # Add confidence interval
          add.params = list(color = "blue",
                            fill = "lightgray")
          )+
  stat_cor(method = "pearson", label.x = 1.5, label.y = 4.5)  # Add correlation coefficient

# Add concentration ellipses
# Change the ellipse type to 'convex'
ggscatter(heteromanova, x = "Drug", y = "DEPTHscore",
          color = "SAMPLESET", palette = "rainbow",
          shape = "SAMPLESET",
          ellipse = TRUE, ellipse.type = "convex")

# Add point labels
rownames(heteromanova) <- heteromanova$SAMPLE 
heteromanova$SAMPLE <- rownames(heteromanova) 
ggscatter(heteromanova, x = "Drug", y = "DEPTHscore",
   color = "SAMPLESET", palette = "rainbow",
   label = "SAMPLE", repel = FALSE)

# Select some labels to show
ggscatter(heteromanova, x = "Drug", y = "DEPTHscore",
   color = "SAMPLESET", palette = "rainbow",
   label = "SAMPLE", repel = TRUE,
   label.select = c("names_of_subclones", "names_of_subclones", "names_of_subclones", "names_of_subclones", "names_of_subclones"))