check updates

DESCRIPTION

@@ -41,6 +41,7 @@ Imports:
     dplyr (>= 0.8.0.1),
     ggplot2 (>= 3.1.1),
     ggrepel (>= 0.8.1),
+    grr,
     htmlwidgets (>= 1.3),
     igraph (>= 1.2.4),
     irlba (>= 2.3.3),
@@ -67,6 +68,7 @@ Imports:
     RhpcBLASctl,
     Rtsne (>= 0.15),
     S4Vectors,
+    shiny,
     slam (>= 0.1-45),
     spdep (>= 1.1-2),
     speedglm (>= 0.3-2),
@@ -78,6 +80,7 @@ Imports:
     viridis (>= 0.5.1)
 Suggests: 
     testthat (>= 2.1.0),
+    ggrastr,
     knitr,
     rmarkdown,
     spelling

R/cluster_cells.R

@@ -125,13 +125,13 @@ cluster_cells <- function(cds,
 #' the implementation, it also scales well, and can be run on graphs of
 #' millions of nodes (as long as they can fit in memory). The core function is
 #' find_partition which finds the optimal partition using the Louvain algorithm
-#' [1] for a number of different methods. The methods currently implemented are
-#' (1) modularity [2], (2) Reichardt and Bornholdt's model using the
-#' configuration null model and the Erdös-Rényi null model [3], (3) the
-#' constant Potts model (CPM) [4], (4) Significance [5], and finally (5)
-#' Surprise [6]. In addition, it supports multiplex partition optimization
-#' allowing community detection on for example negative links [7] or multiple
-#' time slices [8]. It also provides some support for community detection on
+#' for a number of different methods. The methods currently implemented are
+#' (1) modularity, (2) Reichardt and Bornholdt's model using the
+#' configuration null model and the Erdös-Rényi null model, (3) the
+#' constant Potts model (CPM), (4) Significance, and finally (5)
+#' Surprise. In addition, it supports multiplex partition optimization
+#' allowing community detection on for example negative links or multiple
+#' time slices. It also provides some support for community detection on
 #' bipartite graphs. See the documentation for more information." Please see
 #' the github above for the citations. Right now we only support
 #' CPMVertexPartition, RBConfigurationVertexPartition, RBERVertexPartition,

R/order_cells.R

@@ -358,7 +358,7 @@ select_trajectory_roots <- function(cds, x=1, y=2, # nocov start
                                             y = source_prin_graph_dim_2,
                                             yend = target_prin_graph_dim_2)) +
           labs(x="Component 1", y="Component 2") +
-          monocle3:::monocle_theme_opts()
+          monocle_theme_opts()
       }, height = function() {
         session$clientData$output_plot1_width
       })

R/plotting.R

@@ -1476,7 +1476,7 @@ plot_genes_by_group <- function(cds,
     g <- g + xlab(group_cells_by)
   }
 
-  g <- g + ylab("Gene") + monocle3:::monocle_theme_opts() +
+  g <- g + ylab("Gene") + monocle_theme_opts() +
     theme(axis.text.x = element_text(angle = 30, hjust = 1))
   if(axis_order == 'marker_group') {
     g <- g + coord_flip()

R/select_cells.R

@@ -486,7 +486,7 @@ plot_principal_graph <- function(data_df,
   }
 
   g <- g +
-    monocle3:::monocle_theme_opts() +
+    monocle_theme_opts() +
     xlab(paste(reduction_method, 1)) +
     ylab(paste(reduction_method, 2)) +
     theme(legend.key = element_blank()) +