Merge pull request #248 from saeyslab/unit-test

Unit testing

.github/workflows/check-bioc.yml

@@ -49,9 +49,9 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - { os: ubuntu-latest, r: '4.1', bioc: '3.13', cont: "bioconductor/bioconductor_docker:RELEASE_3_13", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
-          - { os: macOS-latest, r: '4.1', bioc: '3.13'}
-          - { os: windows-latest, r: '4.1', bioc: '3.13'}
+          - { os: ubuntu-latest, r: '4.3.2', bioc: '3.18', cont: "bioconductor/bioconductor_docker:RELEASE_3_18", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
+          - { os: macOS-latest, r: '4.3.2', bioc: '3.18'}
+          - { os: windows-latest, r: '4.3.2', bioc: '3.18'}
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       RSPM_ROOT: https://packagemanager.posit.co.

R/application_visualization.R

@@ -546,14 +546,9 @@ make_heatmap_bidir_lt_ggplot = function(matrix, y_name, x_name, y_axis = TRUE, x
 #'
 #' @description \code{make_mushroom_plot} Make a plot in which each glyph consists of two semicircles corresponding to ligand- and receptor- information. The size of the semicircle is the percentage of cells that express the protein, while the saturation corresponds to the scaled average expression value.
 #'
-#' @usage
-#' make_mushroom_plot(prioritization_table, top_n = 30, show_ranking = FALSE, show_all_datapoints = FALSE, true_color_range = FALSE, size = "scaled_avg_exprs", color = "scaled_lfc",
-#'  ligand_fill_colors = c("#DEEBF7", "#08306B"), receptor_fill_colors = c("#FEE0D2", "#A50F15"),
-#'  unranked_ligand_fill_colors = c(alpha("#FFFFFF", alpha=0.2), alpha("#252525", alpha=0.2)), unranked_receptor_fill_colors = c( alpha("#FFFFFF", alpha=0.2), alpha("#252525", alpha=0.2)))
-#'
 #' @param prioritization_table A prioritization table as generated by \code{\link{generate_prioritization_tables}}
 #' @param top_n An integer indicating how many ligand-receptor pairs to show
-#' @param show_ranking A logical indicating whether to show the ranking of the ligand-receptor pairs (default: FALSE)
+#' @param show_rankings A logical indicating whether to show the ranking of the ligand-receptor pairs (default: FALSE)
 #' @param show_all_datapoints A logical indicating whether to show all ligand-receptor pairs (default: FALSE, if true they will be grayed out)
 #' @param true_color_range A logical indicating whether to use the true color range for the ligand-receptor pairs (default: FALSE; range 0-1 is used)
 #' @param size A string indicating which column to use for the size of the semicircles (default: "scaled_avg_exprs"; use column name without "_ligand" or "_receptor" suffix)
@@ -576,13 +571,13 @@ make_heatmap_bidir_lt_ggplot = function(matrix, y_name, x_name, y_axis = TRUE, x
 #' # Create a prioritization table
 #' prior_table <- generate_prioritization_tables(processed_expr_table, processed_DE_table, ligand_activities, processed_condition_markers, prioritizing_weights)
 #' make_mushroom_plot(prior_table)
-#' 
+#'
 #' # Show only top 20, and write rankings on the plot
-#' make_mushroom_plot(prior_table, top_n = 20, show_ranking = TRUE)
-#' 
+#' make_mushroom_plot(prior_table, top_n = 20, show_rankings = TRUE)
+#'
 #' # Show all datapoints, and use true color range
 #' make_mushroom_plot(prior_table, show_all_datapoints = TRUE, true_color_range = TRUE)
-#' 
+#'
 #' # Change the size and color columns
 #' make_mushroom_plot(prior_table, size = "pct_expressed", color = "scaled_avg_exprs")
 #' }

R/data.R

@@ -152,12 +152,13 @@
 "annotation_data_sources"
 #' Optimized data source weights
 #'
-#' A data.frame/tibble describing weight associated to each data source. These weights will be used for weighted aggregagion of source networks and were determined via parameter optimization (see NicheNet paper).
+#' A data.frame/tibble describing weight associated to each data source. These weights will be used for weighted aggregation of source networks and were determined via parameter optimization (see NicheNet paper).
 #'
 #' @format A data frame/tibble
 #' \describe{
 #'   \item{source}{name of data source}
-#'   \item{weight}{weight associated to a data source}
+#'   \item{avg_weight}{weight associated to a data source, averaged over cross-validation folds}
+#'   \item{median_weight}{weight associated to a data source, averaged over cross-validation folds}
 #'   }
 #'
 "optimized_source_weights_df"

tests/testthat/test-application_prediction.R

@@ -5,6 +5,7 @@ test_that("Seurat wrapper works", {
   lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
   weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
   seurat_object_lite = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj_test.rds"))
+  seurat_object_lite = Seurat::UpdateSeuratObject(seurat_object_lite)
 
   nichenet_output = nichenet_seuratobj_aggregate(seurat_obj = seurat_object_lite, receiver = "CD8 T", condition_oi = "LCMV", condition_reference = "SS", condition_colname = "aggregate", sender = c("Mono"), ligand_target_matrix = ligand_target_matrix, weighted_networks = weighted_networks, lr_network = lr_network)
   expect_type(nichenet_output,"list")

tests/testthat/test-differential_nichenet.R

@@ -8,6 +8,8 @@ test_that("Differential NicheNet pipeline works", {
   lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% distinct(ligand, receptor)
 
   seurat_object_lite = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj_test.rds"))
+  seurat_object_lite = Seurat::UpdateSeuratObject(seurat_object_lite)
+
   seurat_object_lite@meta.data$celltype_aggregate = paste(seurat_object_lite@meta.data$celltype, seurat_object_lite@meta.data$aggregate,sep = "_") # user adaptation required on own dataset
 
   celltype_id = "celltype_aggregate" # metadata column name of the cell type of interest

tests/testthat/test-prioritization.R

@@ -5,6 +5,7 @@ test_that("Prioritization scheme works", {
     lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
     weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
     seurat_obj_test = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj_test.rds"))
+    seurat_obj_test = Seurat::UpdateSeuratObject(seurat_obj_test)
 
     lr_network = lr_network %>% distinct(from, to)
     weighted_networks_lr = weighted_networks$lr_sig %>% inner_join(lr_network, by = c("from","to"))