Update gene data based on the suggested ranking

This also exports the ranking function itself for external use.

R/ranking.R

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+#' Rank genes based on how ubiquitous they are.
+#'
+#' This function will compute a weighted average across multiple metrics that
+#' define how ubiquitous a gene is based on its expression across samples.
+#'
+#' @param cross_sample_metric Metric to use for calculating the number of
+#'   samples a gene is expressed in. One of `above_95`, `above_median` or
+#'   `above_zero`.
+#' @param cross_sample_weight Weighting of the cross sample metric within the
+#'   final score.
+#' @param mean_expression_weight Weighting of the gene's mean expression within
+#'   the final score.
+#' @param sd_expression_weight Weighting of the standard deviation of the
+#'   gene's expression within the final score.
+#'
+#' @return A `data.table` with gene data as well as the scores, ranks and
+#'   percentiles for each gene.
+#'
+#' @export
+rank_genes <- function(cross_sample_metric = "above_95",
+                       cross_sample_weight = 0.5,
+                       mean_expression_weight = 0.25,
+                       sd_expression_weight = -0.25) {
+  total_weight <- cross_sample_weight +
+    mean_expression_weight +
+    sd_expression_weight
+
+  data <- copy(ubigen::genes)
+
+  data[, score :=
+    (cross_sample_weight * get(cross_sample_metric) +
+      mean_expression_weight * mean_expression_normalized +
+      sd_expression_weight * sd_expression_normalized) /
+      total_weight]
+
+  # Normalize scores to be between 0.0 and 1.0.
+  data[, score := (score - min(score, na.rm = TRUE)) /
+    (max(score, na.rm = TRUE) - min(score, na.rm = TRUE))]
+
+  # These are genes that are not expressed at all.
+  data[is.na(score), score := 0.0]
+
+  setorder(data, -score)
+  data[, rank := .I]
+  data[, percentile := 1 - rank / max(rank)]
+
+  data
+}