Annotation terms: q-values, size and coefficient of variation
In CALANGO: Comparative Analysis with Annotation-Based Genomic Components

#Prepare data.frame to plot
#Plot
p <- ggplot2::ggplot(data    = df_cutoff,
                     mapping = ggplot2::aes(x           = -log10(corrected_contrasts),
                                            y           = -log10(Spearman_qvalue),
                                            label       = name,
                                            description = description,
                                            cv          = 1 / cv)) +
  ggplot2::geom_point(ggplot2::aes(size = size, alpha = cv)) +
  ggplot2::theme_bw() + 
  ggplot2::ggtitle("Spearman") +
  ggplot2::labs(x = "-log10(Corrected contrasts)",
                y = '-log10(Spearman correlation q-value)')
pl1 <- plotly::ggplotly(p, 
                        tooltip = c('size','name','description','x','y'))

p <- ggplot2::ggplot(data    = df_cutoff,
                     mapping = ggplot2::aes(x           = -log10(corrected_contrasts),
                                            y           = -log10(Kendall_qvalue),
                                            label       = name,
                                            description = description,
                                            cv          = 1 / cv)) +
  ggplot2::geom_point(ggplot2::aes(size = size, alpha = cv)) +
  ggplot2::theme_bw() + 
  ggplot2::ggtitle("Kendall") + 
  ggplot2::labs(x = "-log10(Corrected contrasts)",
                y = '-log10(Kendall correlation q-value)')
pl2 <- plotly::ggplotly(p, 
                        tooltip = c('x','y','size','name','description'))

p <- ggplot2::ggplot(data = df_cutoff,
                     mapping = ggplot2::aes(x           = -log10(corrected_contrasts),
                                            y           = -log10(Pearson_qvalue),
                                            label       = name,
                                            description = description,
                                            cv          = 1 / cv)) +
  ggplot2::geom_point(ggplot2::aes(size = size, alpha = cv)) +
  ggplot2::theme_bw() + 
  ggplot2::ggtitle("Pearson") +
  ggplot2::labs(x = "-log10(Corrected contrasts)",
                y = '-log10(Pearson correlation q-value)')

pl3 <- plotly::ggplotly(p,
                        tooltip = c('size','name','description','x','y'))

htmltools::tagList(pl1, pl2, pl3)