data("count_table", package = "ScreenR") data("annotation_table", package = "ScreenR") data <- count_table annotaion <- annotation_table groups <- factor(c( "T1/T2", "T1/T2", "Treated", "Treated", "Treated", "Control", "Control", "Control", "Treated", "Treated", "Treated", "Control", "Control", "Control" )) palette <- c( "#1B9E75", "#1B9E75", "#D95F02", "#D95F02", "#D95F02", "#7570B3", "#7570B3", "#7570B3", "#E7298A", "#E7298A", "#E7298A", "#66A61E", "#66A61E", "#66A61E" ) create_test_object <- function() { data <- data %>% dplyr::filter(Barcode != "*") colnames(data) <- c( "Barcode", "T1", "T2", "Time3_TRT_A", "Time3_TRT_B", "Time3_TRT_C", "Time3_A", "Time3_B", "Time3_C", "Time4_TRT_A", "Time4_TRT_B", "Time4_TRT_C", "Time4_A", "Time4_B", "Time4_c" ) obj <- create_screenr_object( table = data, annotation = annotaion, groups = groups, replicates = c("") ) obj <- normalize_data(obj) obj <- compute_data_table(obj) obj@data_table <- obj@data_table %>% dplyr::filter(Gene %in% paste0("Gene_", seq(1, 10))) obj@normalized_count_table <- obj@normalized_count_table %>% dplyr::filter(Barcode %in% obj@data_table$Barcode) obj@count_table <- obj@count_table %>% dplyr::filter(Barcode %in% obj@data_table$Barcode) obj@annotation_table <- obj@annotation_table %>% dplyr::filter(Barcode %in% obj@data_table$Barcode) return(obj) } test_that("ROAST", { object <- create_test_object() matrix <- model.matrix(~ object@groups) colnames(matrix) <- c("Control", "T1/T2", "Treated") roast_hit <- suppressWarnings(find_roast_hit( screenR_Object = object, matrix_model = matrix, contrast = "Treated" )) expect_equal(class(roast_hit)[[1]], "tbl_df") }) test_that("Camera", { object <- create_test_object() matrix <- model.matrix(~ object@groups) colnames(matrix) <- c("Control", "T1/T2", "Treated") camera_hit <- suppressWarnings(find_camera_hit( screenR_Object = object, matrix_model = matrix, contrast = "Treated" )) expect_equal(class(camera_hit)[1], "tbl_df") }) test_that("Hit Z-score per giorno", { object <- create_test_object() # In order to speed up the test we will compute the metrics only for a # subset of the genes genes <- c("Gene_1", "Gene_5") object@data_table <- object@data_table[object@data_table$Gene %in% genes, ] table <- compute_metrics(object, treatment = "TRT", control = "Time3", day = "Time3" ) hit_table <- find_zscore_hit(table, number_barcode = 2) expect_equal(class(hit_table)[1], "tbl_df") }) test_that("Find_Zscore_hit mean", { object <- create_test_object() genes <- c("Gene_1", "Gene_5") object@data_table <- object@data_table[object@data_table$Gene %in% genes, ] table <- compute_metrics(object, control = "Time3", treatment = "TRT", day = c("Time3") ) hit_zscore <- find_zscore_hit(table, number_barcode = 2, metric = "mean") expect_equal(class(hit_zscore)[[1]], "tbl_df") }) test_that("Find_Score_hit median ", { object <- create_test_object() genes <- c("Gene_1", "Gene_5") object@data_table <- object@data_table[object@data_table$Gene %in% genes, ] table <- compute_metrics(object, control = "Time3", treatment = "TRT", day = c("Time3") ) hit_zscore <- find_zscore_hit(table, number_barcode = 4) expect_equal(class(hit_zscore)[[1]], "tbl_df") }) test_that("find_robust_zscore_hit median ", { object <- create_test_object() genes <- c("Gene_1", "Gene_5") object@data_table <- object@data_table[object@data_table$Gene %in% genes, ] table <- compute_metrics(object, control = "Time3", treatment = "TRT", day = c("Time3") ) hit_zscore_R <- find_robust_zscore_hit(table, number_barcode = 2) expect_equal(class(hit_zscore_R)[[1]], "grouped_df") })