fix: handle rownames in heatmap plot

CCBR · Feb 7, 2025 · 0a4ffd7 · 0a4ffd7
1 parent 6c01565
commit 0a4ffd7
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Showing 4 changed files with 24 additions and 15 deletions.
diff --git a/R/batch-correction.R b/R/batch-correction.R
@@ -35,7 +35,7 @@
 #'   batch_correct_counts(
 #'     count_type = "norm",
 #'     sub_count_type = "voom",
-#'     covariates_colname = "Group",
+#'     covariates_colnames = "Group",
 #'     batch_colname = "Batch",
 #'     label_colname = "Label"
 #'   )
@@ -160,9 +160,10 @@ batch_correct_counts <- function(moo,
       counts_dat = combat_edata,
       sample_metadata = sample_metadata,
       sample_id_colname = sample_id_colname,
+      feature_id_colname = feature_id_colname,
+      group_colname = group_colname,
       label_colname = label_colname,
-      anno_column = group_colname,
-      anno_colors = colors_for_plots
+      color_values = colors_for_plots
     ) + ggplot2::labs(caption = "batch-corrected counts")
 
     print(pca_plot)

diff --git a/R/heatmap.R b/R/heatmap.R
@@ -5,7 +5,8 @@
 #' @return heatmap ggproto object
 #' @keywords internal
 #'
-plot_heatmap <- function(counts_dat, sample_metadata,
+plot_heatmap <- function(counts_dat,
+                         sample_metadata,
                          sample_id_colname = NULL,
                          feature_id_colname = NULL,
                          group_colname = "Group",
@@ -44,7 +45,11 @@ plot_heatmap <- function(counts_dat, sample_metadata,
   old <- sample_metadata[[sample_id_colname]]
   new <- sample_metadata[[label_colname]]
   names(old) <- new
-  counts_dat <- dplyr::rename(counts_dat, tidyselect::any_of(old))
+  counts_dat %<>% dplyr::rename(tidyselect::any_of(old))
+  if (feature_id_colname %in% colnames(counts_dat)) {
+    counts_dat %<>%
+      tibble::column_to_rownames(var = feature_id_colname)
+  }
 
   mat <- as.matrix(counts_dat)
   tcounts <- t(mat)

diff --git a/R/normalize.R b/R/normalize.R
@@ -127,22 +127,24 @@ normalize_counts <- function(moo,
     ) + ggplot2::labs(caption = "normalized counts")
     print(pca_plot)
     hist_plot <- plot_histogram(
-      df.voom,
-      sample_metadata,
-      feature_id_colname,
-      group_colname,
-      label_colname,
+      counts_dat = df.voom,
+      sample_metadata = sample_metadata,
+      sample_id_colname = sample_id_colname,
+      feature_id_colname = feature_id_colname,
+      group_colname = group_colname,
+      label_colname = label_colname,
       color_values = colors_for_plots,
       x_axis_label = "Normalized Counts"
     ) + ggplot2::labs(caption = "normalized counts")
     print(hist_plot)
     corHM_plot <- plot_heatmap(
       counts_dat = df.filt,
       sample_metadata = sample_metadata,
-      anno_colors = colors_for_plots,
-      anno_column = group_colname,
+      sample_id_colname = sample_id_colname,
+      feature_id_colname = feature_id_colname,
+      group_colname = group_colname,
       label_colname = label_colname,
-      sample_id_colname = sample_id_colname
+      color_values = colors_for_plots
     ) + ggplot2::labs(caption = "normalized counts")
     print(corHM_plot)
   }

diff --git a/tests/testthat/test-batch-correction.R b/tests/testthat/test-batch-correction.R
@@ -14,9 +14,10 @@ test_that("batch_correction works for NIDAP", {
     batch_correct_counts(
       count_type = "norm",
       sub_count_type = "voom",
-      covariates_colname = "Group",
+      covariates_colnames = "Group",
       batch_colname = "Batch",
-      label_colname = "Label"
+      label_colname = "Label",
+      print_plots = TRUE
     )
   # TODO: getting different results than nidap_batch_corrected_counts
   expect_true(all.equal(