Use newly computed metrics

R/ranking.R

@@ -3,34 +3,26 @@
 #' 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) {
+                       level_metric = "median_expression_normalized",
+                       level_weight = 0.25,
+                       variation_metric = "qcv_expression_normalized",
+                       variation_weight = -0.25) {
   total_weight <- abs(cross_sample_weight) +
-    abs(mean_expression_weight) +
-    abs(sd_expression_weight)
+    abs(level_weight) +
+    abs(variation_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) /
+      level_weight * get(level_metric) +
+      variation_weight * get(variation_metric)) /
       total_weight]
 
   # Normalize scores to be between 0.0 and 1.0.