modify the the plot_cluster_scores function to return ggplot object

R/plot_clustering_QC.R

@@ -58,103 +58,46 @@ plot_cluster_freq = function(x) {
 
 #===============================================================================
 
-# plot the final score for a cluster and single type
-# a : scores data-frame
-# b : context type (single value)
-# c : cluster name
-plot_scores_aux1_1 = function(a, b, c) {
-
-  # filter on context type and cluster name
-  df = a %>% subset(type == b & cluster == c)
-
-  # find the quantile value
-  # val = df %>% dplyr::pull(score) %>% quantile(probs = c(0.9))
-  # df1 =df[c(1,2,6)]
-
-  p = ggplot(df, aes(x=signature, y=score, color=cluster, group=cluster)) +
-    geom_line() +
-    geom_point() +
-    geom_hline(yintercept=df$score_quantile, linetype="dashed", color="black") +
-    labs(title=paste0("Signatures Score (Final) in Cluster: ", b), x="Mutational Signature", y="Value") +
-    theme(axis.text.x=element_text(angle=90)) +
-    theme_bw() + guides(color = guide_legend(title = "Clustering Scores"))
 
+plot_cluster_scores <- function(x, context_type, cluster_label, final_score=TRUE) {
+
+  if ( !(cluster_label %in% get_cluster_labels(x)) ) {
+    warning("invalid cluster label!")
+  }
+
+  scores <- get_clusters_score(x, types = context_type, exposure_thr = 0.05, quantile_thr = 0.9)
+  df = scores %>% subset(type == context_type & cluster == cluster_label)
+
+  if (final_score) {
+    # FINAL SCORE
+    p = ggplot(df, aes(x=signature, y=score, color=cluster, group=cluster)) +
+      geom_line() +
+      geom_point() +
+      geom_hline(yintercept=df$score_quantile, linetype="dashed", color="black") +
+      labs(title=paste0("Signatures Score (Final) in Cluster: ", b), x="Mutational Signature", y="Score") +
+      theme(axis.text.x=element_text(angle=90)) +
+      theme_bw() + guides(color = guide_legend(title = "Clustering Scores"))
+  } else if (!final_score) {
+    # PARTIAL SCORES
+    df_long = tidyr::pivot_longer(df[c(1,2,3,4,5)],
+                                  cols=c(varRatio, activeRatio, mutRatio),
+                                  names_to="score_title", values_to="value")
+    p <- ggplot(subset(df_long, cluster == cluster_label),
+                aes(x=signature, y=value, color=score_title, group=score_title)) +
+      geom_line() +
+      geom_point() +
+      labs(
+        title=paste0("Signatures Score (Ingredients) in Cluster: ", cluster_label), 
+        x="Mutational Signature", 
+        y="Value"
+      ) +
+      theme_bw()
+  } else {warning("invalid final_score!")}
+
   return(p)
 }
 
 
-# plot the final score for a cluster and all context types
-# a : scores data-frame
-# b : context types (multiple)
-# c : cluster name
-plot_scores_aux1 = function(a, b, c) {
 
-  plot_list = lapply(b, function(tid) plot_scores_aux1_1(a, tid, c))
 
-  p = patchwork::wrap_plots(
-    plot_list,
-    nrow=length(plot_list),
-    ncol=1,
-    guides="collect",
-    tag_level="keep",
-    design="AAAA\nBBBB"
-  ) & theme(axis.text.x=element_text(angle=90))
-
-  return(p)
-}
-
-
-# plot the scores ingredients for single cluster
-# a : scores data-frame
-# b : single context type
-# c : cluster name
-plot_scores_aux2_1 = function(a, b, c) {
-
-  df = a %>% subset(type == b & cluster == c)
-
-  df_long = tidyr::pivot_longer(df[c(1,2,3,4,5)],
-                                cols=c(varRatio, activeRatio, mutRatio),
-                                names_to="name", values_to="value")
-  p = ggplot(subset(df_long, cluster == c),
-             aes(x=signature, y=value, color=name, group=name)) +
-    geom_line() +
-    geom_point() +
-    labs(title=paste0("Signatures Score (Ingredients) in Cluster: ", b), x="Mutational Signature", y="Value") +
-    theme_bw()
-  return(p)
-}
-
-
-plot_scores_aux2 = function(a, b, c) {
-
-  plot_list = lapply(b, function(tid) plot_scores_aux2_1(a, tid, c))
-
-  p = patchwork::wrap_plots(
-    plot_list,
-    nrow=length(plot_list),
-    ncol=1,
-    guides="collect",
-    tag_level="keep",
-    design="AAAA\nBBBB",
-    axes="collect"
-  )
-
-  return(p)
-}
-
-
-plot_cluster_scores = function(x, clusters=get_cluster_labels(x),
-                               types=get_types(x), min_exposure=0.05,
-                               quantile_thr=0.9) {
-
-  a = get_clusters_score(x, types, min_exposure, quantile_thr)
-
-  lapply(clusters, function(cluster_name) {
-    p1 = plot_scores_aux1(a=a, b=types, c=cluster_name)
-    p2 = plot_scores_aux2(a=a, b=types, c=cluster_name)
-    p3 = plot_exposures(x, types, clusters=cluster_name, min_exposure=min_exposure)
-
-    return( (p1 | p2) / p3 )
-  }) %>% setNames(clusters)
-}