diff --git a/R/sysdata.rda b/R/sysdata.rda
index c785d0c..fcc3232 100644
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/data/genes.rda b/data/genes.rda
index 5e2ad3a..147c955 100644
Binary files a/data/genes.rda and b/data/genes.rda differ
diff --git a/scripts/analyze.R b/scripts/analyze.R
index 800d49b..ab99ebf 100644
--- a/scripts/analyze.R
+++ b/scripts/analyze.R
@@ -7,21 +7,54 @@ library(here)
 
 i_am("scripts/input.R")
 
-data <- fread(here("scripts", "input", "data_long.csv"))
+data <- fread(here("scripts", "input", "data_long.csv.gz"))
 
 data[, `:=`(
     expression_median = median(expression),
     expression_95 = quantile(expression, probs = 0.95)
 ), by = sample]
 
+# Transform the expression logarithmically. The samples that don't express a
+# gene at all will be left out intentionally.
+data[expression > 0, expression_log := log2(expression)]
+
 results <- data[, .(
-    median_expression = median(expression),
-    mean_expression = mean(expression),
-    sd_expression = sd(expression),
+    median_expression = median(expression[expression > 0]),
+    iqr_expression = IQR(expression[expression > 0]),
+    mean_expression = mean(expression[expression > 0]),
+    sd_expression = sd(expression[expression > 0]),
+    median_expression_normalized = median(expression_log, na.rm = TRUE),
+    iqr_expression_normalized = IQR(expression_log, na.rm = TRUE),
+    mean_expression_normalized = mean(expression_log, na.rm = TRUE),
+    sd_expression_normalized = sd(expression_log, na.rm = TRUE),
     above_zero = mean(expression > 0.0),
     above_threshold = mean(expression > 50.0),
     above_median = mean(expression > expression_median),
     above_95 = mean(expression > expression_95)
 ), by = "gene"]
 
+# Normalize values to the range of 0.0 to 1.0.
+results[, `:=`(
+    median_expression_normalized =
+        (median_expression_normalized -
+            min(median_expression_normalized, na.rm = TRUE)) /
+            (max(median_expression_normalized, na.rm = TRUE) -
+                min(median_expression_normalized, na.rm = TRUE)),
+    iqr_expression_normalized =
+        (iqr_expression_normalized -
+            min(iqr_expression_normalized, na.rm = TRUE)) /
+            (max(iqr_expression_normalized, na.rm = TRUE) -
+                min(iqr_expression_normalized, na.rm = TRUE)),
+    mean_expression_normalized =
+        (mean_expression_normalized -
+            min(mean_expression_normalized, na.rm = TRUE)) /
+            (max(mean_expression_normalized, na.rm = TRUE) -
+                min(mean_expression_normalized, na.rm = TRUE)),
+    sd_expression_normalized =
+        (sd_expression_normalized -
+            min(sd_expression_normalized, na.rm = TRUE)) /
+            (max(sd_expression_normalized, na.rm = TRUE) -
+                min(sd_expression_normalized, na.rm = TRUE))
+)]
+
 fwrite(results, file = here("scripts", "output", "results.csv"))
diff --git a/scripts/input.R b/scripts/input.R
index 1d686ba..d1ff95d 100644
--- a/scripts/input.R
+++ b/scripts/input.R
@@ -31,8 +31,8 @@ fwrite(
   file = here(
     "scripts",
     "input",
-    "data_wide_samples.csv"
+    "data_wide_samples.csv.gz"
   )
 )
 
-fwrite(data_long, file = here("scripts", "input", "data_long.csv"))
+fwrite(data_long, file = here("scripts", "input", "data_long.csv.gz"))
diff --git a/scripts/genes.R b/scripts/ranking.R
similarity index 77%
rename from scripts/genes.R
rename to scripts/ranking.R
index ec41d7f..5312786 100644
--- a/scripts/genes.R
+++ b/scripts/ranking.R
@@ -8,11 +8,8 @@ i_am("scripts/input.R")
 
 data <- fread(here("scripts", "output", "results.csv"))
 
-data[, `:=`(
-  gene = stringr::str_split(gene, "\\.") |> purrr::map_chr(1),
-  mean_expression_normalized = mean_expression / max(mean_expression),
-  sd_expression_normalized = sd_expression / max(sd_expression)
-)]
+# Keep only the actual Ensembl ID for each gene.
+data[, gene := stringr::str_split(gene, "\\.") |> purrr::map_chr(1)]
 
 data[, score := 0.5 * above_95 +
   0.25 * mean_expression_normalized +
@@ -22,7 +19,8 @@ data[, score := 0.5 * above_95 +
 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.
+# These are genes that are not expressed at all or expressed just once, in case
+# the standard deviation is used in the score.
 data[is.na(score), score := 0.0]
 
 setorder(data, -score)