## ----setup, include=FALSE--------------------------------- knitr::opts_chunk$set( collapse = TRUE, eval = TRUE, comment = "##" ) options(width=60) library(SimiCviz) ## ----echo = FALSE----------------------------------------- simic_full <- readRDS(system.file("extdata", file.path("simic_full.rds"), package = "SimiCviz")) ## ----eval = FALSE, results='hold'------------------------- # library(SimiCviz) # # # Load entire SimiCPipeline run automatically # simic_full <- load_SimiCPipeline( # project_dir = "path/to/simic_run", # run_name = "example1", # lambda1 = "0.01", # lambda2 = "0.001" # ) # # # Set display names and colors for visualization (Part 3) # simic_full <- setLabelNames( # simic_full, # label_names = c("control", "PD-L1", "DAC", "Combination"), # colors = c("#e0e0e0", "#a8c8ff", "#ffb6b6", "#c1a9e0") # ) ## --------------------------------------------------------- simic_full ## --------------------------------------------------------- # Load weights from pickle weights_file <- system.file("extdata", file.path("outputSimic/example1_simic_weights.pickle"), package = "SimiCviz") simic_weights <- read_weights_pickle(weights_file) simic_weights[[1]][, 1:6] ## --------------------------------------------------------- # Load GRN weights (method agnostic) weight_path <- system.file("extdata", "example_weights.csv", package = "SimiCviz") # Read as data.frame weights_df <- read_weights_csv(weight_path) head(weights_df) # If your method uses different column names you need to rename them ## --------------------------------------------------------- # Load from CSV (recommended format: columns 'cell', 'label') cell_labels_path <- system.file("extdata", file.path("inputFiles", "treatment_annotation.csv"), package = "SimiCviz") cell_labels <- load_cell_labels(cell_labels_path, header = TRUE, sep = ",") head(cell_labels) ## ----eval=FALSE------------------------------------------- # # From vector (will generate cell_1, cell_2, ... names) # cell_labels <- c(0, 0, 1, 1, 2, 2) # Must match order of AUC rows # # # From named vector # cell_labels <- c(cell_A = 0, cell_B = 0, cell_C = 1) # # # From data.frame # cell_labels <- data.frame(cell = c("cell_A", "cell_B"), label = c(0, 1)) ## --------------------------------------------------------- # Load from multiple formats expression_mat_path <- system.file("extdata", file.path("inputFiles", "example1_expression.pickle"), package = "SimiCviz") # Will auto-detect format and load expression_mat <- load_expression_matrix(expression_mat_path) print(class(expression_mat)) print(dim(expression_mat)) ## --------------------------------------------------------- # From .pickle SimiC files # Extract Adjusted R squared from SimiC outputs out <- read_pickle(weights_file) adjusted_r_squared <- out$adjusted_r_squared viz_obj_simic <- SimiCvizExperiment( weights = simic_weights, auc = NULL, # Will compute this in the next section later but can be loaded as well cell_labels = cell_labels, label_names = c("control","PD-L1","DAC","Combination"), colors = c("#e0e0e0", "#a8c8ff", "#ffb6b6", "#c1a9e0"), meta=list(adjusted_r_squared=adjusted_r_squared)) viz_obj_simic ## --------------------------------------------------------- # From SimiC input files # adj_r2_threshold: For SimiCPipeline style outputs it will look in metadata of viz_obj_simic, for "simic@meta$adjusted_r_squared" and filter out targets with lower R²) # n_cores: Number of workers if backend = 'multissession' or # Number of cores if backend = 'multicore' viz_obj_simic <- calculate_activity_scores( viz_obj_simic, expression = expression_mat_path, adj_r2_threshold = 0.7, # For SimiC style outputs sort_by="expression", # Rank targets by expression or weight select_top_k = NULL, # Use all targets (or limit to top K) percent_of_target = 1.0, n_cores = 2, backend = "multicore", verbose = TRUE ) # Access computed scores auc_scores <- viz_obj_simic@auc$collected head(auc_scores[, 1:5]) ## --------------------------------------------------------- # Initialize processor with weights and expression AS_processor <- AUCProcessor( weights = weights_df, expression = expression_mat_path, # or matrix/data.frame cell_labels = cell_labels, qc_type = "adj_p_val", qc_threshold = 0.05, # Filter targets above this threshold n_cores = 2, backend = "multisession" ) # Compute with custom parameters AS_processor <- compute_auc( AS_processor, sort_by = "expression", select_top_k = NULL, # Use all targets (or limit to top K) percent_of_target = 1.0, # Use all targets (or subset %) verbose = TRUE ) ## --------------------------------------------------------- # Extract results in wide format (default) auc_wide <- get_auc(AS_processor, format = "wide") head(auc_wide) ## --------------------------------------------------------- # Extract results long format auc_long <- get_auc(AS_processor, format = "long") # Long format head(auc_long) ## ----eval = F--------------------------------------------- # weights_df <- read.csv("path/to/your/weights.csv") # weights_df_filtered <- weights_df[weights_df$p_value < 0.01,] # # From CSV files # viz_obj <- SimiCvizExperiment( # weights = weights_df_filtered, # auc = NULL, # Will compute this inthe next section later but can be loaded as well # cell_labels = cell_labels, # label_names = c("control","PD-L1","DAC","Combination"), # colors = c("#e0e0e0", "#a8c8ff", "#ffb6b6", "#c1a9e0"), # meta=list() # Anything you want to store in a list format # ) # # viz_obj # viz_obj <- calculate_activity_scores( # viz_obj, # expression = expression_mat_path, # adj_r2_threshold = 0.7, # For SimiC style outputs # sort_by="expression", # Rank targets by expression or weight # select_top_k = NULL, # Use all targets (or limit to top K) # percent_of_target = 1.0, # n_cores = 2, # backend = "multisession", # verbose = TRUE # ) ## ----eval = FALSE----------------------------------------- # # SimiC: Filter by adjusted R² (goodness of fit) # # processor_simic <- AUCProcessor( # weights = simic_weights, # expression = expr_mat, # cell_labels = cell_labels, # adj_r2_list = adjusted_r_squared, # a list length as simic_weights # qc_type = "adj_r2", # qc_threshold = 0.7 # Keep targets with R² ≥ 0.7 # ) # # # SCENIC / Pando: Filter by adjusted p-value # processor_scenic <- AUCProcessor( # weights = weights_df, # expression = expr_mat, # cell_labels = cell_labels, # qc_type = "p_value", # qc_threshold = 0.05, # Keep targets with adj_p_val ≤ 0.05 # n_cores = 4, # backend = "multisession" # ) # # # Compute with the same data, different parameters # processor_scenic <- compute_auc(processor_scenic, sort_by = "weight") ## --------------------------------------------------------- # Recall above examples simic_full # Complete SimiCpipeline output viz_obj_simic # SimiCPipeline weights -> `calculate_activity_scores` ## --------------------------------------------------------- # Create SimiCvizExperiment simic <- SimiCvizExperiment(weights = simic_weights, auc = auc_wide, cell_labels = cell_labels, label_names = c("control","PD-L1","DAC","Combination"), colors = c("#e0e0e0", "#a8c8ff", "#ffb6b6", "#c1a9e0")) simic ## ----echo = FALSE----------------------------------------- plot_dir <- file.path(getwd(),"SimiCviz_output","plots") ## ----eval = FALSE----------------------------------------- # plot_dir <- file.path(getwd(),"SimiCviz_output") # dir.create(plot_dir,recursive = TRUE) ## --------------------------------------------------------- # Plot distribution of adjusted R² values across targets # Extract Adjusted R squared from SimiC outputs out <- read_pickle(weights_file) adjusted_r_squared <- out$adjusted_r_squared plot_r2_distribution(adjusted_r_squared, simic, grid = c(2, 2), save = FALSE, out_dir = plot_dir) ## --------------------------------------------------------- # Select targets by adjusted R2 unselected_targets <- list() selected_targets <- list() lab_keys <- names(simic@label_names) for (lab in lab_keys){ # Save selected for plotting selected_targets[[lab]] <- simic@target_ids[which(adjusted_r_squared[[lab]] >= 0.7)] # Save unselected for reporting label <- simic@label_names[[lab]] unselected_targets[[label]] <- simic@target_ids[which(adjusted_r_squared[[lab]] < 0.7)] } print("Number of unselected targets per label:") print(sapply(unselected_targets, length)) ## ----fig.height=10, fig.width=10-------------------------- plot_tf_weights( simic, tf_names = simic@tf_ids[1:4], top_n = 25, allowed_targets = selected_targets, # Filter by R² if desired grid = c(2, 2), save = FALSE, out_dir = plot_dir, filename = "TF_weights_barplot.pdf" ) ## ----fig.height=5, fig.width=5---------------------------- plot_target_weights( simic, target_names = simic@target_ids[1:4], labels = c("control", "Combination"), grid = c(2, 2), save = FALSE, out_dir = plot_dir, filename = "Target_weights_barplot.pdf" ) ## ----include=FALSE---------------------------------------- all_tfs_barplots <- plot_tf_weights( simic, top_n = 25, grid = NULL, allowed_targets = selected_targets) ## ----eval = F--------------------------------------------- # all_tfs_barplots <- plot_tf_weights( # simic, # top_n = 25, # grid = NULL, # allowed_targets = selected_targets) ## --------------------------------------------------------- all_tfs_barplots[[1]] ## --------------------------------------------------------- network <- get_tf_network(simic, "Tet2", r2_threshold = 0.7) print(head(network)) plot_tf_network_heatmap(simic, "Tet2", save = FALSE, top_n = 15, r2_threshold = 0.7, show_values = TRUE, cmap = c("purple","white","yellow")) ## ----collapse=TRUE, results='hold'------------------------ dis_score <- calculate_dissimilarity(simic) top_tfs <- rownames(dis_score) ## --------------------------------------------------------- plot_dissimilarity_heatmap(simic, top_n = 5, cmap = "viridis", save = FALSE) ## --------------------------------------------------------- metadata <- read.csv(system.file("extdata/metadata.csv", package = "SimiCviz")) # Build cell groups from metadata (e.g. Seurat clusters, cell types, etc.) cell_groups <- lapply(unique(metadata$final_annotation_functional), function(celltype) { cell_labels$cell[metadata$final_annotation_functional == celltype] }) names(cell_groups) <- unique(metadata$final_annotation_functional) dissim_grouped <- calculate_dissimilarity(simic, labels = c(0,2), cell_groups = cell_groups) # For all labels plot_dissimilarity_heatmap(simic, cell_groups = cell_groups, top_n = 5, cmap=c("magma"), save = FALSE, out_dir = plot_dir, filename = "dissimilarity_heatmap_grouped.pdf") ## --------------------------------------------------------- # For labels 0,2 plot_dissimilarity_heatmap(simic, labels = c(0,2), cell_groups = cell_groups, top_n = 5, sort_by = "Proliferating.cells", cmap=c("red", "white", "blue"), save = FALSE) ## ----fig.height=10, fig.width=10-------------------------- # Plot distributions for top TFs plot_auc_distributions( simic, tf_names = top_tfs[1:4], fill = TRUE, alpha = 0.6, bw_adjust = 1/8, rug = TRUE, save = FALSE, out_dir = plot_dir, filename = "AUC_distributions.pdf", grid = c(2, 2) ) ## ----fig.height=5, fig.width=10--------------------------- # Plot top 4 TFs density distributions plot_auc_distributions(simic, labels = c(0,3), tf_names = top_tfs[1:2], fill = FALSE, bw_adjust = 0.5, rug = FALSE, out_dir = plot_dir, filename="AUC_distributions_notfilled_multipage.pdf", save = FALSE, grid = c(1,2)) ## ----fig.height=10, fig.width=10-------------------------- plot_auc_cumulative( simic, tf_names = top_tfs[1:4], rug = TRUE, grid = c(2, 2), include_table = TRUE, save = FALSE, out_dir = plot_dir ) ## ----eval=TRUE-------------------------------------------- ecdf_metrics <- calculate_ecdf_auc(simic, tf_names = simic@tf_ids[1:4]) head(ecdf_metrics) ## --------------------------------------------------------- plot_auc_heatmap(simic, top_n = 20) ## ----eval=TRUE-------------------------------------------- summary_plot <- plot_auc_summary_statistics(simic) ## --------------------------------------------------------- sessionInfo()