Remove select_se() and replace with embracing where required.

cole-trapnell-lab · Mar 30, 2024 · 721d664 · 721d664
1 parent 818262b
commit 721d664
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Showing 9 changed files with 55 additions and 270 deletions.
diff --git a/.github/workflows/check_on_push.yml b/.github/workflows/check_on_push.yml
@@ -121,7 +121,6 @@ jobs:
       #    Rscript -e 'install.packages(c("ggdist", "ggforce"))'
       - name: Run R CMD build and R CMD check
         run: |
-          Rscript -e 'install.packages(c("rlang"))'
           R CMD build .
           R CMD check monocle3*tar.gz
         shell: bash

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -93,7 +93,6 @@ Imports:
     RColorBrewer,
     Rcpp (>= 1.0.1),
     reshape2 (>= 1.4.3),
-    rlang (>= 1.1.3),
     rsample (>= 0.0.5),
     RhpcBLASctl,
     RcppAnnoy,

diff --git a/NAMESPACE b/NAMESPACE
@@ -1,7 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(select_se,data.frame)
-S3method(select_se,grouped_df)
 export("fData<-")
 export("pData<-")
 export("principal_graph<-")
@@ -72,7 +70,6 @@ export(save_transform_models)
 export(search_cds_nn_index)
 export(search_nn_index)
 export(search_nn_matrix)
-export(select_se)
 export(set_cds_nn_index)
 export(set_cds_row_order_matrix)
 export(set_matrix_control)

diff --git a/R/cluster_cells.R b/R/cluster_cells.R
@@ -599,6 +599,8 @@ compute_partitions <- function(g,
                                optim_res,
                                qval_thresh=0.05,
                                verbose = FALSE){
+  # The cells membership may have information about the
+  # clusters to which cells' nearest neighbors belong.
   cell_membership <- as.factor(igraph::membership(optim_res))
   membership_matrix <- Matrix::sparse.model.matrix( ~ cell_membership + 0)
   num_links <- Matrix::t(membership_matrix) %*%
@@ -617,6 +619,10 @@ compute_partitions <- function(g,
 
   num_links <- num_links_ij / total_edges
 
+  # Deal with zero total edges.
+  num_links[is.nan(num_links)] <- 0
+  cluster_mat[is.nan(cluster_mat)] <- 0
+
   cluster_mat <- matrix(stats::p.adjust(cluster_mat),
                         nrow=length(louvain_modules),
                         ncol=length(louvain_modules))

diff --git a/R/deprec_dplyr.R b/R/deprec_dplyr.R
diff --git a/R/order_cells.R b/R/order_cells.R
@@ -245,30 +245,32 @@ select_trajectory_roots <- function(cds, x=1, y=2, # nocov start
   if (use_3d){
     edge_df <- dp_mst %>%
       igraph::as_data_frame() %>%
-      monocle3::select_se(source = "from", target = "to") %>%
+      # select() and select_() behave the same with these arguments.
+      dplyr::select(source = "from", target = "to") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(source="sample_name",
+                         dplyr::select(source="sample_name",
                                     source_prin_graph_dim_1="prin_graph_dim_1",
                                     source_prin_graph_dim_2="prin_graph_dim_2",
                                     source_prin_graph_dim_3="prin_graph_dim_3"),
                        by = "source") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(target="sample_name",
+                         dplyr::select(target="sample_name",
                                     target_prin_graph_dim_1="prin_graph_dim_1",
                                     target_prin_graph_dim_2="prin_graph_dim_2",
                                     target_prin_graph_dim_3="prin_graph_dim_3"),
                        by = "target")
   }else{
     edge_df <- dp_mst %>%
       igraph::as_data_frame() %>%
-      monocle3::select_se(source = "from", target = "to") %>%
+      # select() and select_() behave the same with these arguments.
+      dplyr::select(source = "from", target = "to") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(source="sample_name",
+                         dplyr::select(source="sample_name",
                                     source_prin_graph_dim_1="prin_graph_dim_1",
                                     source_prin_graph_dim_2="prin_graph_dim_2"),
                        by = "source") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(target="sample_name",
+                         dplyr::select(target="sample_name",
                                     target_prin_graph_dim_1="prin_graph_dim_1",
                                     target_prin_graph_dim_2="prin_graph_dim_2"),
                        by = "target")

diff --git a/R/plotting.R b/R/plotting.R
@@ -284,29 +284,31 @@ plot_cells_3d <- function(cds,
   ## Graph info
   if (show_trajectory_graph) {
 
+    # select() and select_() behave differently here so embrace x, y, and z.
     ica_space_df <- t(cds@principal_graph_aux[[reduction_method]]$dp_mst) %>%
       as.data.frame() %>%
-      monocle3::select_se(prin_graph_dim_1 = x, prin_graph_dim_2 = y,
-                          prin_graph_dim_3 = z) %>%
+      dplyr::select(prin_graph_dim_1 = {{x}}, prin_graph_dim_2 = {{y}},
+                     prin_graph_dim_3 = {{z}}) %>%
       dplyr::mutate(sample_name = rownames(.),
                     sample_state = rownames(.))
 
     dp_mst <- cds@principal_graph[[reduction_method]]
 
+    # select() and select_() behave the same with these arguments.
     edge_df <- dp_mst %>%
       igraph::as_data_frame() %>%
-      monocle3::select_se(source = "from", target = "to") %>%
+      dplyr::select(source = "from", target = "to") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(source="sample_name",
-                                             source_prin_graph_dim_1="prin_graph_dim_1",
-                                             source_prin_graph_dim_2="prin_graph_dim_2",
-                                             source_prin_graph_dim_3="prin_graph_dim_3"),
+                         dplyr::select(source="sample_name",
+                                        source_prin_graph_dim_1="prin_graph_dim_1",
+                                        source_prin_graph_dim_2="prin_graph_dim_2",
+                                        source_prin_graph_dim_3="prin_graph_dim_3"),
                        by = "source") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(target="sample_name",
-                                             target_prin_graph_dim_1="prin_graph_dim_1",
-                                             target_prin_graph_dim_2="prin_graph_dim_2",
-                                             target_prin_graph_dim_3="prin_graph_dim_3"),
+                         dplyr::select(target="sample_name",
+                                        target_prin_graph_dim_1="prin_graph_dim_1",
+                                        target_prin_graph_dim_2="prin_graph_dim_2",
+                                        target_prin_graph_dim_3="prin_graph_dim_3"),
                        by = "target")
 
     if(nrow(edge_df) < 1) warning('bad loop: nrow(edge_df) < 1')
@@ -547,25 +549,27 @@ plot_cells <- function(cds,
   ## Graph info
   if (show_trajectory_graph) {
 
+    # select() and select_() behave differently here so embrace x and y.
     ica_space_df <- t(cds@principal_graph_aux[[reduction_method]]$dp_mst) %>%
       as.data.frame() %>%
-      monocle3::select_se(prin_graph_dim_1 = x, prin_graph_dim_2 = y) %>%
+      dplyr::select(prin_graph_dim_1 = {{x}}, prin_graph_dim_2 = {{y}}) %>%
       dplyr::mutate(sample_name = rownames(.),
                     sample_state = rownames(.))
 
     dp_mst <- cds@principal_graph[[reduction_method]]
 
+    # select() and select_() behave the same with these arguments.
     edge_df <- dp_mst %>%
       igraph::as_data_frame() %>%
-      monocle3::select_se(source = "from", target = "to") %>%
+      dplyr::select(source = "from", target = "to") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(
+                         dplyr::select(
                            source="sample_name",
                            source_prin_graph_dim_1="prin_graph_dim_1",
                            source_prin_graph_dim_2="prin_graph_dim_2"),
                        by = "source") %>%
       dplyr::left_join(ica_space_df %>%
-                         monocle3::select_se(
+                         dplyr::select(
                            target="sample_name",
                            target_prin_graph_dim_1="prin_graph_dim_1",
                            target_prin_graph_dim_2="prin_graph_dim_2"),
@@ -1883,12 +1887,14 @@ plot_genes_by_group <- function(cds,
       g <- ggplot(ExpVal, aes(y = Gene,  x = Group)) +
         geom_point(aes(colour = group_color_class,  size = mean)) +
         #viridis::scale_color_viridis(name = 'percentage') +
-        scale_size(name = 'log(mean + 0.1)', range = c(0, max.size))
+        #scale_size(name = 'log(mean + 0.1)', range = c(0, max.size))
+        scale_size(name = paste0('log(mean + ', pseudocount, ')'), range = c(0, max.size))
     }else{
       g <- ggplot(ExpVal, aes(y = Gene,  x = Group)) +
         geom_point(aes(colour = percentage,  size = mean)) +
         viridis::scale_color_viridis(name = 'percentage') +
-        scale_size(name = 'log(mean + 0.1)', range = c(0, max.size))
+        #scale_size(name = 'log(mean + 0.1)', range = c(0, max.size))
+        scale_size(name = paste0('log(mean + ', pseudocount, ')'), range = c(0, max.size))
     }
   } else {
     if (color_by_group){
@@ -1899,7 +1905,8 @@ plot_genes_by_group <- function(cds,
     }else{
       g <- ggplot(ExpVal, aes(y = Gene,  x = Group)) +
         geom_point(aes(colour = mean,  size = percentage)) +
-        viridis::scale_color_viridis(name = 'log(mean + 0.1)') +
+        #viridis::scale_color_viridis(name = 'log(mean + 0.1)') +
+        viridis::scale_color_viridis(name = paste0('log(mean + ', pseudocount, ')')) +
         scale_size(name = 'percentage', range = c(0, max.size))
     }
   }