scripts: Add gsea_vs_cmap.R

scripts/gsea_vs_cmap.R

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+library(data.table)
+library(here)
+
+i_am("scripts/gsea_vs_cmap.R")
+
+ranking_gtex <- ubigen::rank_genes(ubigen::gtex_all)
+ranking_cmap <- ubigen::rank_genes(ubigen::cmap)
+
+data <- merge(
+  ranking_gtex[, .(gene, score, percentile)],
+  ranking_cmap[, .(gene, score, percentile)],
+  by = "gene",
+  suffixes = c(x = "_gtex", y = "_cmap")
+)
+
+cat("N =", nrow(data))
+
+# OPTICS clustering
+
+result_optics <- dbscan::optics(
+  data[, .(score_gtex, score_cmap)],
+  minPts = 0.01 * nrow(data)
+)
+
+result_cluster <- result_optics |> dbscan::extractXi(0.05)
+data[, cluster := result_cluster$cluster]
+
+cluster_means <- data[,
+  .(
+    score_gtex = mean(score_gtex),
+    score_cmap = mean(score_cmap)
+  ),
+  by = cluster
+]
+
+cluster_means[, distance_1_1 := sqrt(
+  (1.0 - score_gtex)^2 + (1.0 - score_cmap)^2
+)]
+
+cluster_1_1 <- cluster_means[which.min(distance_1_1), cluster]
+genes_optics <- data[cluster == cluster_1_1, gene]
+write(genes_optics, here("scripts/output/genes_optics.txt"))
+
+# Percentiles
+
+genes_1_1 <- data[percentile_gtex >= 0.95 & percentile_cmap >= 0.95, gene]
+genes_1_0 <- data[percentile_gtex >= 0.95 & percentile_cmap < 0.95, gene]
+
+write(genes_1_1, here("scripts/output/genes_1_1.txt"))
+write(genes_1_0, here("scripts/output/genes_1_0.txt"))
+
+threshold_gtex <- data[percentile_gtex >= 0.95, min(score_gtex)]
+threshold_cmap <- data[percentile_cmap >= 0.95, min(score_cmap)]
+
+fig <- plotly::plot_ly() |>
+  plotly::add_markers(
+    data = data,
+    x = ~score_gtex,
+    y = ~score_cmap,
+    name = ~cluster,
+    showlegend = FALSE,
+    marker = list(size = 4),
+    cliponaxis = FALSE
+  ) |>
+  plotly::layout(
+    xaxis = list(
+      title = "Ranking based on GTEx",
+      range = c(0, 1)
+    ),
+    yaxis = list(
+      title = "Ranking based on CMap",
+      range = c(0, 1)
+    ),
+    annotations = list(
+      list(
+        text = "95%",
+        x = threshold_gtex,
+        y = 1,
+        xshift = 2,
+        yshift = 3,
+        yref = "paper",
+        xanchor = "left",
+        yanchor = "top",
+        showarrow = FALSE
+      ),
+      list(
+        text = "95%",
+        x = 1,
+        y = threshold_cmap,
+        yshift = 2,
+        xref = "paper",
+        xanchor = "right",
+        yanchor = "bottom",
+        showarrow = FALSE
+      )
+    ),
+    shapes = list(
+      list(
+        type = "line",
+        y0 = 0,
+        y1 = 1,
+        yref = "paper",
+        x0 = threshold_gtex,
+        x1 = threshold_gtex,
+        line = list(
+          color = "#00000080",
+          opacity = 0.5,
+          width = 1,
+          dash = "dot"
+        )
+      ),
+      list(
+        type = "line",
+        y0 = threshold_cmap,
+        y1 = threshold_cmap,
+        x0 = 0,
+        x1 = 1,
+        xref = "paper",
+        line = list(
+          color = "#00000080",
+          width = 1,
+          opacity = 0.5,
+          dash = "dot"
+        )
+      )
+    ),
+    font = list(size = 8),
+    margin = list(
+      pad = 2,
+      l = 36,
+      r = 0,
+      t = 0,
+      b = 36
+    )
+  )
+
+# Plotly specifies all sizes in pixels, including font size. Because of
+# that, we can actually think of these pixels as points. One point is defined as
+# 1/72 inch and SVG uses 96 DPI as the standard resolution.
+#
+# 1 plotly pixel = 1 point = 1/72 inch = 1 1/3 actual pixels
+#
+# So, we specify width and height in points (= plotly pixels) and scale up the
+# image by 96/72 to convert everything from points to pixels at 96 DPI.
+
+plotly::save_image(
+  fig,
+  file = here("scripts/output/gsea_vs_cmap.svg"),
+  width = 6.27 * 72,
+  height = 6.27 * 72,
+  scale = 96 / 72
+)