Created
March 12, 2025 04:40
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| # Load required libraries | |
| library(cmapR) # For handling GCT files | |
| library(ComplexHeatmap) # For visualization, see https://jokergoo.github.io/ComplexHeatmap-reference/book/ for a complete reference | |
| library(RColorBrewer) # For color palettes | |
| library(dplyr) | |
| make_random_gct <- function(nrow = 100, ncol = 10) { | |
| set.seed(369) | |
| nrow <- max(nrow, 1) | |
| ncol <- max(ncol, 1) | |
| # Define Sample Groups and Batches | |
| sample_groups <- sample(c("A", "B", "C"), ncol, replace = TRUE) | |
| batch_numbers <- sample(1:3, ncol, replace = TRUE) | |
| # Generate base pattern index (same for all columns) | |
| base_index <- seq(1, nrow) / max(nrow) * 2 * pi | |
| # Create an empty matrix and fill it based on group assignment | |
| expr_matrix <- matrix(0, nrow = nrow, ncol = ncol) | |
| # Vectorized pattern assignment | |
| expr_matrix[, sample_groups == "A"] <- sin(base_index) | |
| expr_matrix[, sample_groups == "B"] <- -cos(base_index) | |
| expr_matrix[, sample_groups == "C"] <- sin(base_index) - cos(base_index) | |
| # Add random noise | |
| expr_matrix <- expr_matrix + matrix(rnorm(nrow * ncol, mean = 0, sd = 0.5), nrow = nrow, ncol = ncol) | |
| # Apply batch effect (small shift per batch) | |
| expr_matrix <- expr_matrix + (matrix(batch_numbers - 2, nrow = nrow, ncol = ncol, byrow = TRUE) * 0.5) | |
| # Assign sample & gene identifiers | |
| .rids <- paste0("Gene", seq_len(nrow)) | |
| .cids <- paste0("Sample", seq_len(ncol)) | |
| # Metadata (samples) | |
| .cdesc <- data.frame( | |
| Group = sample_groups, | |
| Batch = as.factor(batch_numbers) | |
| ) | |
| rownames(.cdesc) <- .cids # Required for cmapR | |
| # Metadata (genes) | |
| .rdesc <- data.frame( | |
| Gene_Type = sample(c("Protein Coding", "Pseudogene", "lncRNA"), nrow, replace = TRUE) | |
| ) | |
| rownames(.rdesc) <- .rids # Required for cmapR | |
| # Create GCT object | |
| gct <- cmapR::GCT(mat = expr_matrix, rid = .rids, cid = .cids, cdesc = .cdesc, rdesc = .rdesc) | |
| return(gct) | |
| } | |
| ## == | |
| # 📌 Generate a Random GCT Object | |
| gct <- make_random_gct(nrow = 3400, ncol = 22) | |
| # 🔍 Extract Expression Matrix & Metadata | |
| expr_matrix <- gct@mat # The gene expression values | |
| sample_annotations <- gct@cdesc # Column (sample) metadata | |
| # 🏷️ Define Heatmap Annotations (Sample Groups) | |
| annotation_colors <- list( | |
| Group = c("A" = "blue", "B" = "red", "C" = "green"), | |
| Batch = c("1" = "purple", "2" = "orange", "3" = "yellow") | |
| ) | |
| ha <- HeatmapAnnotation(df = sample_annotations, col = annotation_colors) | |
| # 🎨 Define a Heatmap Color Palette | |
| heatmap_palette <- colorRampPalette(c("blue", "grey80", "red"))(100) # | |
| # 🔥 Create the Heatmap | |
| Heatmap( | |
| expr_matrix, | |
| name = "Expression", | |
| top_annotation = ha, | |
| col = heatmap_palette, | |
| cluster_rows = TRUE, # Cluster genes | |
| cluster_columns = TRUE, # Cluster samples | |
| show_row_names = FALSE, # Hide gene names for clarity | |
| show_column_names = TRUE, # Show sample names | |
| column_names_side = "top", | |
| # column_split = gct@cdesc$Group | |
| ) |
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