add example and test for abVolPlot

NAMESPACE

@@ -62,6 +62,7 @@ importFrom(grDevices,dev.off)
 importFrom(grDevices,pdf)
 importFrom(magrittr,"%>%")
 importFrom(methods,new)
+importFrom(rstatix,wilcox_test)
 importFrom(stats,as.formula)
 importFrom(stats,model.matrix)
 importFrom(stats,p.adjust)

R/visualization.R

@@ -368,26 +368,36 @@ stackedPlot <- function(object, x_axis, y_axis, x_order, y_order, color, width,
 #' @param dir_output directory to save the output plot (default: ".")
 #' @return save volcano abundance plot
 #' @examples
-#' \dontrun{
-#' abVolPlot(
-#'   object = aie_sct,
-#'   cluster_idents = "predicted.id",
-#'   sample = "sample",
-#'   cluster_order = unique(aie_sct$predicted.id),
-#'   group_by = "AIE_type",
-#'   group1 = "LGI1",
-#'   group2 = "control",
-#'   color = dittoColors(),
-#'   min_pct = 0.5
+#' library(Seurat)
+#' library(rstatix)
+#' set.seed(123)
+#' pbmc_small$predicted.id <- sample(c("Cluster1", "Cluster2"), ncol(pbmc_small), replace = TRUE)
+#' pbmc_small$sample <- sample(c("Sample1", "Sample2"), ncol(pbmc_small), replace = TRUE)
+#' pbmc_small$AIE_type <- sample(c("LGI1", "control"), ncol(pbmc_small), replace = TRUE)
+#' suppressWarnings(
+#'     plot <- abVolPlot(
+#'         object = pbmc_small,
+#'         cluster_idents = "predicted.id",
+#'         sample = "sample",
+#'         cluster_order = c("Cluster1", "Cluster2"),
+#'         group_by = "AIE_type",
+#'         group1 = "LGI1",
+#'         group2 = "control",
+#'         color = c("Cluster1" = "blue", "Cluster2" = "red"),
+#'         width = 5,
+#'         height = 5,
+#'         dir_output = "."
+#'     )
 #' )
-#' }
-#' @export
+#' unlink("volcano_plot_predicted.id_pbmc_small_LGI1_control.pdf")
 #' @importFrom dplyr across left_join mutate
 #' @importFrom ggplot2 aes geom_hline geom_point geom_vline ggplot ggsave scale_color_manual theme theme_classic xlab ylab
 #' @importFrom stats wilcox.test
 #' @importFrom tibble rownames_to_column tibble
 #' @importFrom tidyr pivot_longer
 #' @importFrom tidyselect where
+#' @importFrom rstatix wilcox_test
+#' @export
 
 abVolPlot <- function(object, cluster_idents, sample, cluster_order, group_by, group1, group2, color, width = 5, height = 5, min_cells = 10, paired = FALSE, dir_output = ".") {
   if (!inherits(object, "Seurat")) {
@@ -406,7 +416,7 @@ abVolPlot <- function(object, cluster_idents, sample, cluster_order, group_by, g
 
   for (i in cluster_order) {
     out1 <- cl_size_ind[cl_size_ind$cluster == i, ]
-    pvalue_res[i] <- wilcox.test(count ~ group_by, data = out1, paired = paired)$p.value
+    pvalue_res[i] <- wilcox_test(count ~ group_by, data = out1, paired = paired)$p
   }
 
   pvalue_cl <- data.frame(cluster = cluster_order, pvalue = pvalue_res)

tests/testthat/test_visualization.R

@@ -194,3 +194,65 @@ test_that("stackedPlot works as expected", {
     # Cleanup: Remove the generated file
     unlink("stacked_barplot_pbmc_small_disease.pdf")
 })
+
+test_that("abVolPlot works as expected", {
+    library(Seurat)
+    library(rstatix)
+    set.seed(123)
+    pbmc_small$predicted.id <- sample(c("Cluster1", "Cluster2"), ncol(pbmc_small), replace = TRUE)
+    pbmc_small$sample <- sample(c("Sample1", "Sample2"), ncol(pbmc_small), replace = TRUE)
+    pbmc_small$AIE_type <- sample(c("LGI1", "control"), ncol(pbmc_small), replace = TRUE)
+
+    # Run the function
+    suppressWarnings(
+        plot <- abVolPlot(
+            object = pbmc_small,
+            cluster_idents = "predicted.id",
+            sample = "sample",
+            cluster_order = c("Cluster1", "Cluster2"),
+            group_by = "AIE_type",
+            group1 = "LGI1",
+            group2 = "control",
+            color = c("Cluster1" = "blue", "Cluster2" = "red"),
+            width = 5,
+            height = 5,
+            dir_output = "."
+        )
+    )
+
+    # Test 1: Function creates a file in the correct directory
+    expect_true(file.exists("volcano_plot_predicted.id_pbmc_small_LGI1_control.pdf"))
+    # Test 2: Function creates a file that is not empty
+    expect_gt(file.info("volcano_plot_predicted.id_pbmc_small_LGI1_control.pdf")$size, 0)
+    # Test 3: Test plot objects
+    expect_s3_class(plot, "ggplot")
+    p <- ggplot2::ggplot_build(plot)
+    expect_equal(
+        p$layout$panel_params[[1]]$x$limits,
+        c(-1, 1)
+    )
+
+    # Test 4: Check if the function throws an error if object is not a Seurat object
+    expect_error(
+        abVolPlot(
+            object = data.frame(a = c(1:3)),
+            cluster_idents = "predicted.id",
+            sample = "sample",
+            cluster_order = c("Cluster1", "Cluster2"),
+            group_by = "AIE_type",
+            group1 = "LGI1",
+            group2 = "control",
+            color = c("Cluster1" = "blue", "Cluster2" = "red"),
+            width = 5,
+            height = 5,
+            dir_output = "."
+        ),
+        "Object must be a Seurat object"
+    )
+
+    # Test 5: Check if the plot has the expected elements
+    expect_true("GeomPoint" %in% sapply(plot$layers, function(x) class(x$geom)[1]))
+
+    # Cleanup: Remove the generated file
+    unlink("volcano_plot_predicted.id_pbmc_small_LGI1_control.pdf")
+})