Create a single Manhattan plot using the tidyplots package

tpManhattanSingle.R

tpManhattanSingle <- function(
    summ.1, chr = "CHR", bp = "BP", p = "P", snp = "SNP",
    summ.1.name = "$-log[10](P)$", plot.label = NULL,
    logp = TRUE, size = 2, lead.snp = NULL, r2 = NULL,
    plot.width = 100, plot.height = 50, ...
) {
  
  if (!is.null(summ.1[[snp]])) {
    d1 <- data.frame(CHR = summ.1[[chr]], BP = summ.1[[bp]], P = summ.1[[p]], SNP = summ.1[[snp]])
  } else {
    d1 <- data.frame(CHR = summ.1[[chr]], BP = summ.1[[bp]], P = summ.1[[p]])
  }
  
  # Calculate -log(P) for summ.1 and summ.2
  if (logp) {
    d1$logp <- -log10(d1$P)
  } else {
    d1$logp <- d1$P
  }
  
  # Map SNP r2 to discrete colors
  # This is inspired by locuscomparer package
  if (is.null(r2)) {
    g1 <- d1 %>% 
      mutate(x = BP / 1e6) %>% 
      tidyplot(x = x, y = logp) %>% 
      adjust_size(width = plot.width, height = plot.height) %>% 
      adjust_font(fontsize = 10) %>% 
      add_data_points(color = "steelblue3", size = 0.5, rasterize = T, rasterize_dpi = 960)
    
  } else {
    d1$r2 <- r2
    
    g1 <- d1 %>% 
      mutate(x = BP / 1e6) %>% 
      tidyplot(x = x, y = logp, col = r2) %>% 
      adjust_size(width = plot.width, height = plot.height) %>% 
      adjust_font(fontsize = 10) %>% 
      add_data_points(size = 0.5, rasterize = T, rasterize_dpi = 960) %>% 
      adjust_colors(new_colors = colors_continuous_turbo, limits = c(0, 1))
  }
  
  g1 <- g1 %>% 
    adjust_title(title = plot.label) %>% 
    add_annotation_line(x = lead.snp / 1e6, xend = lead.snp / 1e6, y = 0, yend = max(d1$logp),
                        color = "grey50", lwd = 0.4, lty = 2) %>% 
    add_data_points(data = filter_rows(BP == lead.snp), shape = 18, size = 3, color = "purple2") %>% 
    adjust_y_axis(title = summ.1.name, limits = c(0, max(d1$logp) * 1.05)) %>% 
    adjust_x_axis(title = "", padding = c(0, 0))
  
  return(g1)
}