Feature updates

.Rbuildignore

@@ -9,3 +9,5 @@
 paper.md
 paper.bib
 ^joss$
+^doc$
+^Meta$

.gitignore

@@ -4,4 +4,6 @@
 .httr-oauth
 .DS_Store
 inst/doc
-.Rprofile
+.Rprofile
+/doc/
+/Meta/

DESCRIPTION

@@ -16,6 +16,7 @@ Imports:
     forcats,
     ggplot2,
     ggplotify,
+    ggprism,
     ggpubr,
     ggrepel,
     grid,
@@ -30,7 +31,7 @@ Imports:
     umap
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Suggests: 
     knitr,
     rmarkdown,

R/PAVER_hunter_plot.R

@@ -5,78 +5,120 @@
 #'
 #' @param PAVER_result a list containing the output of PAVER analysis
 #' @param unit optionally, the unit of enrichment analysis for the figure legend title.
+#' @param show_row_titles a logical indicating whether to show row titles in the heatmap.
 #'
 #' @return A heatmap of the expression data with clustering and color-coded values based on the direction of regulation.
 #'
 #' @examples
 #' TRUE
 #'
 #' @export
-PAVER_hunter_plot <- function(PAVER_result, unit=NULL) {
+PAVER_hunter_plot <- function(PAVER_result, unit=NULL, show_row_titles=TRUE) {
 
   data = PAVER_result$prepared_data %>%
-    tidyr::pivot_wider(names_from = "Group", values_from = c("value"), id_cols = "GOID") %>%
+    tidyr::pivot_wider(
+      names_from = "Group",
+      values_from = c("value"),
+      id_cols = "GOID"
+    ) %>%
     dplyr::inner_join(PAVER_result$clustering %>%
-                        dplyr::select(.data$GOID, .data$Cluster), by = "GOID") %>%
+                        dplyr::select(.data$GOID, .data$Cluster),
+                      by = "GOID") %>%
     dplyr::mutate(Cluster = forcats::fct_drop(.data$Cluster)) %>%
     dplyr::mutate(dplyr::across(
       .cols = dplyr::where(is.numeric),
-      .fns = ~ tidyr::replace_na(.x, 0))) %>%
+      .fns = ~ tidyr::replace_na(.x, 0)
+    )) %>%
     dplyr::distinct(.data$GOID, .keep_all = TRUE)
 
   mat = data %>%
     dplyr::select(dplyr::where(is.numeric)) %>%
     as.matrix()
 
-  min = min(mat, na.rm=T)
-  max = max(mat, na.rm=T)
+  min = min(mat, na.rm = T)
+  max = max(mat, na.rm = T)
 
-  if(nlevels(PAVER_result$prepared_data$Direction) != 1) { #If the data is signed, 0 is the middle
+  if (nlevels(PAVER_result$prepared_data$Direction) != 1) {
+    #If the data is signed, 0 is the middle
     col_fun = circlize::colorRamp2(c(min, 0, max), c("blue", "white", "red"))
     at = c(min, 0, max)
     labels = c(round(min, 2), 0, round(max, 2))
   } else {
     at = c(min, max)
     labels = c(round(min, 2), round(max, 2))
-    if(levels(PAVER_result$prepared_data$Direction) == "+") {
+    if (levels(PAVER_result$prepared_data$Direction) == "+") {
       col_fun = circlize::colorRamp2(c(min, max), c("white", "red"))
     } else {
       col_fun = circlize::colorRamp2(c(min, max), c("white", "blue"))
     }
   }
 
-  lgd = ComplexHeatmap::Legend(col_fun = col_fun,
-               title = unit,
-               at = at,
-               labels = labels,
-               direction = "horizontal",
-               title_position = "lefttop",
-               title_gp = grid::gpar(fontsize = 9,fontface="bold"),
-               labels_gp = grid::gpar(fontsize = 9,fontface="bold"))
-
-  if(ncol(mat) >= 2) {
-    dend = ComplexHeatmap::cluster_within_group(mat %>% t(), data$Cluster)
+  lgd = ComplexHeatmap::Legend(
+    col_fun = col_fun,
+    title = unit,
+    at = at,
+    labels = labels,
+    direction = "horizontal",
+    title_position = "lefttop",
+    title_gp = grid::gpar(fontsize = 8, fontface =
+                            "bold"),
+    labels_gp = grid::gpar(fontsize = 8, fontface =
+                             "bold"),
+    legend_width = grid::unit(1, "cm"),
+  )
+
+  c_data = mat %>% t()
+
+  #If there is only one column, we need to duplicate the data to get the clustering
+  if (ncol(mat) >= 1) {
+    c_data = rbind(c_data, c_data)
   }
 
-  ht = ComplexHeatmap::Heatmap(mat,
-                               col = col_fun,
-               row_title_gp = grid::gpar(fontsize = 9,fontface="bold"),
-               column_names_gp = grid::gpar(fontsize = 9,fontface="bold"),
-               column_names_rot = 0,
-               column_names_centered = TRUE,
-               row_title_rot = 0,
-               row_title_side = "right",
-               row_gap = grid::unit(1, "mm"),
-               show_row_dend = T,
-               cluster_rows = if (ncol(mat) >= 2) dend else T,
-               cluster_columns = ncol(mat) >= 3,
-               split = if (ncol(mat) >= 2) nlevels(data$Cluster) else data$Cluster,
-               show_heatmap_legend = FALSE,
-               border = TRUE)
-
-  ht_draw = ComplexHeatmap::draw(ht,
-       heatmap_legend_list = lgd,
-       heatmap_legend_side="bottom")
+  dend = ComplexHeatmap::cluster_within_group(c_data, data$Cluster)
+
+  color_order = data[stats::order.dendrogram(dend),]$Cluster %>% forcats::fct_inorder() %>% levels()
+
+  plot_colors = PAVER_result$colors %>%
+    magrittr::set_names(levels(data$Cluster))
+
+  plot_colors = plot_colors[color_order]
+
+  cluster_annotation = ComplexHeatmap::rowAnnotation(Cluster = ComplexHeatmap::anno_block(gp = grid::gpar(fill = plot_colors)),
+                                                     width = grid::unit(.25, "cm"))
+
+  args = list(
+    matrix = mat,
+    col = col_fun,
+    left_annotation = cluster_annotation,
+    row_title_gp = grid::gpar(fontsize = 8, fontface = "bold"),
+    column_names_gp = grid::gpar(fontsize = 8, fontface = "bold"),
+    column_names_rot = 0,
+    column_names_centered = TRUE,
+    row_title_rot = 0,
+    row_title_side = "right",
+    row_gap = grid::unit(1, "mm"),
+    show_row_dend = FALSE,
+    show_column_dend = FALSE,
+    cluster_rows = dend,
+    cluster_columns = ncol(mat) >= 3,
+    split = nlevels(data$Cluster),
+    show_heatmap_legend = FALSE,
+    border = TRUE,
+    row_title = NULL
+  )
+
+  if (show_row_titles) {
+    args$row_title = character()
+  }
+
+  ht = do.call(ComplexHeatmap::Heatmap, args)
+
+  ht_draw = ComplexHeatmap::draw(
+    ht,
+    heatmap_legend_list = lgd,
+    heatmap_legend_side = "bottom",
+    padding = grid::unit(c(1, 1, 1, 1), "mm")
+  )
 
   plot = grid::grid.grabExpr(ComplexHeatmap::draw(ht_draw)) %>%
     ggplotify::as.ggplot()

R/PAVER_theme_plot.R

@@ -4,14 +4,15 @@
 #' of the UMAP layout colored by cluster assignments.
 #'
 #' @param PAVER_result a list containing the output of PAVER analysis
+#' @param show_cluster_legend a logical indicating whether to show the cluster legend.
 #'
 #' @return A ggplot2 scatterplot of the UMAP layout colored by cluster assignments
 #'
 #' @examples
 #' TRUE
 #'
 #' @export
-PAVER_theme_plot <- function(PAVER_result) {
+PAVER_theme_plot <- function(PAVER_result, show_cluster_legend=TRUE) {
 
   plot = PAVER_result$umap$layout %>%
     tibble::as_tibble(rownames = NA, .name_repair = "universal_quiet") %>%
@@ -24,7 +25,40 @@ PAVER_theme_plot <- function(PAVER_result) {
                                  colour = .data$Cluster)) +
     ggplot2::geom_point(ggplot2::aes(shape = .data$Group)) +
     ggplot2::scale_color_manual(values = PAVER_result$colors) +
-    ggpubr::theme_pubr(legend = "right")
+    ggprism::theme_prism(base_size = 9) +
+    ggplot2::theme(
+      axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0)),
+      axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0)),
+      axis.text.x = ggplot2::element_blank(),
+      axis.text.y = ggplot2::element_blank(),
+      axis.ticks.x = ggplot2::element_blank(),
+      axis.ticks.y = ggplot2::element_blank(),
+      legend.position = "bottom",
+      legend.direction = "horizontal",
+      legend.spacing.y = grid::unit(-1, 'mm'),
+      legend.spacing.x = grid::unit(-1, 'mm'),
+      legend.box.spacing = grid::unit(-1, 'mm'),
+      legend.key.spacing.x = grid::unit(-1, "mm"),
+      legend.key.spacing.y = grid::unit(-1, "mm"),
+      legend.box.margin = ggplot2::margin(),
+      legend.box = "vertical",
+      panel.grid = ggplot2::element_blank(),
+      panel.border = ggplot2::element_blank(),
+      plot.margin = grid::unit(c(0, 0, 0, 0), "mm"),
+      panel.spacing = grid::unit(c(0, 0, 0, 0), "mm"),
+      legend.margin = ggplot2::margin(0),
+      legend.text = ggplot2::element_text(face = "bold", size = 9, margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0))
+    ) +
+    ggplot2::guides(
+      fill = ggplot2::guide_legend(byrow = T,
+                          override.aes = list(size = 2.5)),
+      shape = ggplot2::guide_legend(byrow = T,
+                           override.aes = list(size = 2.5))) +
+    {if (show_cluster_legend) ggplot2::guides(color = ggplot2::guide_legend(
+      byrow = TRUE,
+      ncol = 1,
+      override.aes = list(shape = 15, size = 2.75)
+    )) else ggplot2::guides(color = "none")}
 
   plot
 }

renv.lock

@@ -1010,6 +1010,26 @@
       ],
       "Hash": "1547863db3b472cf7181973acf649f1a"
     },
+    "ggprism": {
+      "Package": "ggprism",
+      "Version": "1.0.5",
+      "Source": "Repository",
+      "Repository": "RSPM",
+      "Requirements": [
+        "R",
+        "digest",
+        "ggplot2",
+        "glue",
+        "grid",
+        "gtable",
+        "rlang",
+        "scales",
+        "stats",
+        "tibble",
+        "utils"
+      ],
+      "Hash": "6b1020c155c1048c18f39df11eb464e8"
+    },
     "ggpubr": {
       "Package": "ggpubr",
       "Version": "0.6.0",

vignettes/PAVER.Rmd

@@ -42,7 +42,7 @@ PAVER_result = prepare_data(input, embeddings, term2name)
 After preparing your data, PAVER can generate a set of pathway clusters and identify the most representative pathway (theme) for each cluster. The following code chunk demonstrates how to generate pathway clusters using the example data provided in the PAVER package. To constrain the pathway clustering, we pass the following arguments to (dynamicTreeCut)[https://cran.r-project.org/package=dynamicTreeCut]. Increasing `minClusterSize` will result in fewer clusters, while increasing `maxCoreScatter` will result in more clusters. 
 <!-- https://stackoverflow.com/questions/19734381/cutting-dendrogram-into-n-trees-with-minimum-cluster-size-in-r -->
 ```{r}
-minClusterSize = 5
+minClusterSize = 3
 maxCoreScatter = 0.33
 minGap = (1 - maxCoreScatter) * 3 / 4
 PAVER_result = generate_themes(