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| # ============================================================ | |
| # QTL-Mapper v7 ENHANCED — FIXED VERSION | |
| # Fixes: | |
| # 1. Added colourpicker to required_packages | |
| # 2. Customization controls now fully wired to plots & downloads | |
| # 3. make_chromosome_ideogram_reference accepts color/size params | |
| # 4. Manhattan plot height/width driven by sliders (uiOutput) | |
| # 5. Ideogram width now respected in renderPlot | |
| # 6. PNG downloads use custom dimensions & styles | |
| # 7. threshold_style mapped correctly to base R lty values | |
| # 8. compare_bar PNG uses custom dimensions | |
| # 9. All colourInput references safe-guarded with defaults | |
| # ============================================================ | |
| options(repos = c(CRAN = "https://cloud.r-project.org")) | |
| required_packages <- c( | |
| "shiny", "randomForest", "caret", "DT", "ggplot2", "plotly", | |
| "dplyr", "glmnet", "shinyjs", "scales", "e1071", "xgboost", | |
| "writexl", "colourpicker" | |
| ) | |
| for (pkg in required_packages) { | |
| if (!require(pkg, character.only = TRUE, quietly = TRUE)) { | |
| install.packages(pkg, dependencies = TRUE) | |
| library(pkg, character.only = TRUE) | |
| } | |
| } | |
| # ============================================================ | |
| # 6 MODELS | |
| # ============================================================ | |
| MAPPING_MODELS <- list( | |
| "SIM" = list(label = "Simple Interval Mapping (SIM)", desc = "Single-marker regression; LOD = -log10(p)"), | |
| "CIM" = list(label = "Composite Interval Mapping (CIM)", desc = "Cartographer-like CIM with cofactors"), | |
| "MQM" = list(label = "Multiple QTL Model (MQM)", desc = "Forward stepwise multi-QTL selection"), | |
| "RF" = list(label = "Random Forest QTL", desc = "Non-parametric; permutation importance"), | |
| "SVR" = list(label = "Support Vector Regression", desc = "Kernel-based; permutation importance"), | |
| "XGBOOST" = list(label = "XGBoost QTL", desc = "Gradient boosting; gain importance") | |
| ) | |
| MODEL_COLORS <- c( | |
| "SIM" = "#CC0000", | |
| "CIM" = "#FF3333", | |
| "MQM" = "#990000", | |
| "RF" = "#FF6600", | |
| "SVR" = "#CC3300", | |
| "XGBOOST" = "#FF0066" | |
| ) | |
| # ============================================================ | |
| # HELPERS | |
| # ============================================================ | |
| cn <- function(x) trimws(gsub('[\r\n\"]', '', as.character(x))) | |
| calc_genetic_distance <- function(marker1, marker2, marker_info) { | |
| m1 <- marker_info[marker_info$Marker == marker1, ] | |
| m2 <- marker_info[marker_info$Marker == marker2, ] | |
| if (nrow(m1) == 0 || nrow(m2) == 0) return(Inf) | |
| if (m1$Chromosome[1] == m2$Chromosome[1]) return(abs(m1$Position[1] - m2$Position[1])) | |
| return(Inf) | |
| } | |
| # Safe colour getter with fallback | |
| get_color <- function(input_val, fallback) { | |
| if (is.null(input_val) || nchar(trimws(input_val)) == 0) return(fallback) | |
| input_val | |
| } | |
| # ============================================================ | |
| # GENOTYPE LOADER | |
| # ============================================================ | |
| load_genotype_file <- function(filepath) { | |
| lines <- readLines(filepath, warn = FALSE) | |
| lines <- gsub("\r", "", lines) | |
| lines <- lines[nchar(trimws(lines)) > 0] | |
| if (length(lines) < 4) stop("Need >= 4 rows: header + chr + pos + >= 1 sample") | |
| header <- cn(strsplit(lines[1], ",")[[1]]) | |
| chr_row <- cn(strsplit(lines[2], ",")[[1]]) | |
| pos_row <- cn(strsplit(lines[3], ",")[[1]]) | |
| data_lines <- lines[-(1:3)] | |
| gd <- read.csv(text = paste(data_lines, collapse = "\n"), | |
| header = FALSE, stringsAsFactors = FALSE, quote = '"', fill = TRUE) | |
| if (ncol(gd) < length(header)) { | |
| for (k in (ncol(gd)+1):length(header)) gd[[paste0("V",k)]] <- NA | |
| } | |
| if (ncol(gd) > length(header)) gd <- gd[, 1:length(header)] | |
| colnames(gd) <- header | |
| rownames(gd) <- cn(as.character(gd[, 1])) | |
| gd <- gd[, -1, drop = FALSE] | |
| for (col in colnames(gd)) gd[[col]] <- suppressWarnings(as.numeric(as.character(gd[[col]]))) | |
| marker_info <- data.frame( | |
| Marker = colnames(gd), | |
| Chromosome = cn(chr_row[-1][seq_along(colnames(gd))]), | |
| Position = suppressWarnings(as.numeric(cn(pos_row[-1][seq_along(colnames(gd))]))), | |
| stringsAsFactors = FALSE | |
| ) | |
| marker_info <- marker_info[!is.na(marker_info$Position) & marker_info$Chromosome != "", ] | |
| valid_cols <- colnames(gd)[colnames(gd) %in% marker_info$Marker] | |
| gd <- gd[, valid_cols, drop = FALSE] | |
| list(geno = gd, marker_info = marker_info, | |
| n_samples = nrow(gd), n_markers = ncol(gd), | |
| chromosomes = sort(unique(marker_info$Chromosome))) | |
| } | |
| # ============================================================ | |
| # SIM | |
| # ============================================================ | |
| qtl_scan_sim <- function(X_valid, y_valid, marker_info) { | |
| results_list <- list() | |
| for (m in colnames(X_valid)) { | |
| geno <- X_valid[, m] | |
| valid_idx <- !is.na(geno) & !is.na(y_valid) | |
| geno_v <- geno[valid_idx]; pheno_v <- y_valid[valid_idx] | |
| if (length(unique(geno_v)) < 2 || length(pheno_v) < 8) next | |
| m_info <- marker_info[marker_info$Marker == m, ] | |
| if (nrow(m_info) == 0) next | |
| m_chr <- as.character(m_info$Chromosome[1]); m_pos <- m_info$Position[1] | |
| if (is.na(m_pos)) next | |
| tryCatch({ | |
| fit <- lm(pheno_v ~ geno_v) | |
| ct <- coef(summary(fit)) | |
| if (!"geno_v" %in% rownames(ct)) next | |
| p_val <- ct["geno_v", "Pr(>|t|)"]; lod <- -log10(max(p_val, 1e-300)) | |
| a_code <- geno_v - 1; d_code <- as.numeric(geno_v == 1) | |
| has_het <- length(unique(d_code)) > 1 | |
| lm_eff <- if (has_het) lm(pheno_v ~ a_code + d_code) else lm(pheno_v ~ a_code) | |
| pve_m <- max(0, summary(lm_eff)$r.squared * 100) | |
| ce <- coef(lm_eff); se_t <- coef(summary(lm_eff)) | |
| a_eff <- ce["a_code"] | |
| se_a <- if ("a_code" %in% rownames(se_t)) se_t["a_code", "Std. Error"] else NA | |
| d_eff <- if (has_het) ce["d_code"] else NA | |
| d_rat <- if (!is.na(d_eff) && !is.na(a_eff) && a_eff != 0) abs(d_eff / a_eff) else NA | |
| dom_cl <- if (is.na(d_rat)) "Additive" | |
| else if (d_rat > 1) "Overdominance" | |
| else if (d_rat > 0.5) "Dominance" | |
| else if (d_rat > 0.2) "Partial Dom." else "Additive" | |
| results_list[[m]] <- data.frame( | |
| Marker=m, Chromosome=m_chr, Position=m_pos, | |
| LOD=lod, P_Value=p_val, | |
| PVE_Marker_Percent=pve_m, PVE_CIM_Percent=pve_m, | |
| Additive_Effect=a_eff, SE_Additive=se_a, | |
| CI_Lower=ifelse(is.na(se_a),NA,a_eff-1.96*se_a), | |
| CI_Upper=ifelse(is.na(se_a),NA,a_eff+1.96*se_a), | |
| Dominance_Effect=d_eff, Dominance_Ratio=d_rat, Dominance_Class=dom_cl, | |
| N_Cofactors=0, N_Samples=length(pheno_v), Method="SIM", | |
| stringsAsFactors=FALSE) | |
| }, error=function(e) {}) | |
| } | |
| if (length(results_list) == 0) return(data.frame(Message="No results from SIM")) | |
| res <- do.call(rbind, results_list) | |
| res[order(res$Chromosome, res$Position), ] | |
| } | |
| # ============================================================ | |
| # CIM | |
| # ============================================================ | |
| qtl_scan_cim_improved <- function(X_valid, y_valid, marker_info, | |
| window_size = 10, n_cofactors = 5) { | |
| cat("\n=== CIM ===\n") | |
| initial_pvals <- sapply(colnames(X_valid), function(m) { | |
| g <- X_valid[, m]; valid_idx <- !is.na(g) & !is.na(y_valid) | |
| if (sum(valid_idx) < 8 || length(unique(g[valid_idx])) < 2) return(1) | |
| tryCatch(coef(summary(lm(y_valid[valid_idx] ~ g[valid_idx])))[2, 4], error=function(e) 1) | |
| }) | |
| sorted_markers <- names(sort(initial_pvals)) | |
| cofactors <- character(0) | |
| min_distance <- 20 | |
| for (candidate in sorted_markers) { | |
| if (length(cofactors) >= n_cofactors) break | |
| if (initial_pvals[candidate] > 0.01) break | |
| too_close <- FALSE | |
| if (length(cofactors) > 0) { | |
| for (existing_cof in cofactors) { | |
| if (calc_genetic_distance(candidate, existing_cof, marker_info) < min_distance) { | |
| too_close <- TRUE; break | |
| } | |
| } | |
| } | |
| if (!too_close) cofactors <- c(cofactors, candidate) | |
| } | |
| results_list <- list() | |
| for (m in colnames(X_valid)) { | |
| geno <- X_valid[, m]; valid_idx <- !is.na(geno) & !is.na(y_valid) | |
| geno_v <- geno[valid_idx]; pheno_v <- y_valid[valid_idx] | |
| if (length(unique(geno_v)) < 2 || length(pheno_v) < 8) next | |
| m_info <- marker_info[marker_info$Marker == m, ] | |
| if (nrow(m_info) == 0) next | |
| m_chr <- as.character(m_info$Chromosome[1]); m_pos <- m_info$Position[1] | |
| if (is.na(m_pos)) next | |
| chr_markers <- marker_info[marker_info$Chromosome == m_chr, ] | |
| chr_markers <- chr_markers[order(chr_markers$Position), ] | |
| focal_idx <- which(chr_markers$Marker == m) | |
| exclude_markers <- character(0) | |
| if (length(focal_idx) > 0) { | |
| ws <- max(1, focal_idx - window_size); we <- min(nrow(chr_markers), focal_idx + window_size) | |
| exclude_markers <- chr_markers$Marker[ws:we] | |
| } | |
| active_cof <- setdiff(cofactors, c(m, exclude_markers)) | |
| active_cof <- active_cof[active_cof %in% colnames(X_valid)] | |
| tryCatch({ | |
| df <- data.frame(y = pheno_v, focal = geno_v) | |
| if (length(active_cof) > 0) { | |
| cof_matrix <- X_valid[valid_idx, active_cof, drop=FALSE] | |
| cof_matrix[is.na(cof_matrix)] <- 0 | |
| df <- cbind(df, cof_matrix) | |
| } | |
| df <- df[complete.cases(df), ] | |
| if (nrow(df) < 8) return(NULL) | |
| if (length(active_cof) > 0) { | |
| fit_full <- lm(as.formula(paste("y ~ focal +", paste(active_cof, collapse=" + "))), data=df) | |
| fit_reduced <- lm(as.formula(paste("y ~", paste(active_cof, collapse=" + "))), data=df) | |
| } else { | |
| fit_full <- lm(y ~ focal, data=df); fit_reduced <- lm(y ~ 1, data=df) | |
| } | |
| anova_result <- anova(fit_reduced, fit_full) | |
| p_val <- anova_result[2, "Pr(>F)"]; lod <- -log10(max(p_val, 1e-300)) | |
| ct <- coef(summary(fit_full)) | |
| if (!"focal" %in% rownames(ct)) return(NULL) | |
| RSS_full <- sum(residuals(fit_full)^2); RSS_reduced <- sum(residuals(fit_reduced)^2) | |
| pve_cim <- if (RSS_reduced > 0) max(0, (RSS_reduced - RSS_full) / RSS_reduced * 100) else NA | |
| a_code <- df$focal - 1; d_code <- as.numeric(df$focal == 1) | |
| has_het <- length(unique(d_code)) > 1 | |
| lm_effects <- if (has_het) lm(df$y ~ a_code + d_code) else lm(df$y ~ a_code) | |
| pve_marker <- max(0, summary(lm_effects)$r.squared * 100) | |
| ce <- coef(lm_effects); se_table <- coef(summary(lm_effects)) | |
| a_eff <- ce["a_code"] | |
| se_a <- if ("a_code" %in% rownames(se_table)) se_table["a_code", "Std. Error"] else NA | |
| d_eff <- if (has_het) ce["d_code"] else NA | |
| d_ratio <- if (!is.na(d_eff) && !is.na(a_eff) && a_eff != 0) abs(d_eff / a_eff) else NA | |
| dom_class <- if (is.na(d_ratio)) "Additive" | |
| else if (d_ratio > 1) "Overdominance" | |
| else if (d_ratio > 0.5) "Dominance" | |
| else if (d_ratio > 0.2) "Partial Dom." else "Additive" | |
| results_list[[m]] <- data.frame( | |
| Marker=m, Chromosome=m_chr, Position=m_pos, | |
| LOD=lod, P_Value=p_val, | |
| PVE_Marker_Percent=pve_marker, PVE_CIM_Percent=pve_cim, | |
| Additive_Effect=a_eff, SE_Additive=se_a, | |
| CI_Lower=ifelse(is.na(se_a),NA,a_eff-1.96*se_a), | |
| CI_Upper=ifelse(is.na(se_a),NA,a_eff+1.96*se_a), | |
| Dominance_Effect=d_eff, Dominance_Ratio=d_ratio, Dominance_Class=dom_class, | |
| N_Cofactors=length(active_cof), N_Samples=nrow(df), Method="CIM", | |
| stringsAsFactors=FALSE) | |
| }, error=function(e) cat("CIM error at", m, ":", e$message, "\n")) | |
| } | |
| if (length(results_list) == 0) return(data.frame(Message="CIM produced no results")) | |
| res <- do.call(rbind, results_list) | |
| res[order(res$Chromosome, res$Position), ] | |
| } | |
| # ============================================================ | |
| # MQM | |
| # ============================================================ | |
| qtl_scan_mqm <- function(X, y, marker_info) { | |
| valid <- !is.na(y); X_v <- X[valid,,drop=FALSE]; y_v <- y[valid] | |
| p_vals <- sapply(colnames(X_v), function(m) { | |
| g <- X_v[,m]; v2 <- !is.na(g) | |
| if (length(unique(g[v2]))<2||sum(v2)<8) return(1) | |
| tryCatch(coef(summary(lm(y_v[v2]~g[v2])))[2,4],error=function(e) 1) | |
| }) | |
| selected <- character(0); threshold <- 0.01 | |
| for (cand in names(sort(p_vals))[1:min(20,length(p_vals))]) { | |
| test_model <- c(selected, cand) | |
| df_t <- data.frame(y=y_v, X_v[,test_model,drop=FALSE]) | |
| df_t <- df_t[complete.cases(df_t),] | |
| if (nrow(df_t)<10) next | |
| tryCatch({ | |
| fw <- lm(y~.,data=df_t) | |
| wo <- if(length(selected)>0) lm(as.formula(paste("y~",paste(selected,collapse="+"))),data=df_t) else lm(y~1,data=df_t) | |
| p <- anova(wo,fw)[2,"Pr(>F)"] | |
| if (!is.na(p)&&p<threshold) selected <- test_model | |
| },error=function(e){}) | |
| } | |
| if (length(selected)==0) selected <- names(sort(p_vals))[1:min(5,length(p_vals))] | |
| results_list <- list() | |
| for (m in colnames(X_v)) { | |
| g <- X_v[,m]; v2 <- !is.na(g)&!is.na(y_v) | |
| gv <- g[v2]; yv <- y_v[v2] | |
| if (length(unique(gv))<2||length(yv)<8) next | |
| mi <- marker_info[marker_info$Marker==m,] | |
| if (nrow(mi)==0) next | |
| cof_m <- setdiff(selected,m) | |
| tryCatch({ | |
| df2 <- data.frame(y=yv,focal=gv) | |
| if (length(cof_m)>0){cm<-X_v[v2,cof_m,drop=FALSE];cm[is.na(cm)]<-0;df2<-cbind(df2,cm)} | |
| df2 <- df2[complete.cases(df2),] | |
| if (nrow(df2)<8) return(NULL) | |
| ff <- if(length(cof_m)>0) lm(as.formula(paste("y~focal+",paste(cof_m,collapse="+"))),data=df2) else lm(y~focal,data=df2) | |
| fr <- if(length(cof_m)>0) lm(as.formula(paste("y~",paste(cof_m,collapse="+"))),data=df2) else lm(y~1,data=df2) | |
| ct2 <- coef(summary(ff)) | |
| if (!"focal"%in%rownames(ct2)) next | |
| p2 <- ct2["focal","Pr(>|t|)"]; lod2 <- -log10(max(p2,1e-300)) | |
| RF2 <- sum(residuals(ff)^2); RR2 <- sum(residuals(fr)^2) | |
| pvc <- if(RR2>0) max(0,(RR2-RF2)/RR2*100) else NA | |
| ac <- gv-1; dc2 <- as.numeric(gv==1); h2 <- length(unique(dc2))>1 | |
| le <- if(h2) lm(yv~ac+dc2) else lm(yv~ac) | |
| pvm <- max(0,summary(le)$r.squared*100) | |
| ce2 <- coef(le); se2 <- coef(summary(le)) | |
| ae <- ce2["ac"]; sea <- if("ac"%in%rownames(se2)) se2["ac","Std. Error"] else NA | |
| de <- if(h2) ce2["dc2"] else NA | |
| dr <- if(!is.na(de)&&!is.na(ae)&&ae!=0) abs(de/ae) else NA | |
| dc3 <- if(is.na(dr)) "Additive" else if(dr>1) "Overdominance" else if(dr>0.5) "Dominance" else if(dr>0.2) "Partial Dom." else "Additive" | |
| results_list[[m]] <- data.frame( | |
| Marker=m,Chromosome=as.character(mi$Chromosome[1]),Position=mi$Position[1], | |
| LOD=lod2,P_Value=p2,PVE_Marker_Percent=pvm,PVE_CIM_Percent=pvc, | |
| Additive_Effect=ae,SE_Additive=sea, | |
| CI_Lower=ifelse(is.na(sea),NA,ae-1.96*sea),CI_Upper=ifelse(is.na(sea),NA,ae+1.96*sea), | |
| Dominance_Effect=de,Dominance_Ratio=dr,Dominance_Class=dc3, | |
| N_Cofactors=length(cof_m),N_Samples=nrow(df2),Method="MQM",stringsAsFactors=FALSE) | |
| },error=function(e){}) | |
| } | |
| if (length(results_list)==0) return(data.frame(Message="MQM no results")) | |
| res <- do.call(rbind,results_list); res[order(res$Chromosome,res$Position),] | |
| } | |
| # ============================================================ | |
| # RF | |
| # ============================================================ | |
| qtl_scan_rf <- function(X, y, marker_info) { | |
| valid <- !is.na(y)&apply(X,1,function(r) !any(is.na(r))) | |
| X_v <- X[valid,,drop=FALSE]; y_v <- y[valid] | |
| rf_fit <- tryCatch(randomForest(x=X_v,y=y_v,ntree=500,importance=TRUE),error=function(e) NULL) | |
| if (is.null(rf_fit)) return(data.frame(Message="Random Forest failed")) | |
| imp <- importance(rf_fit,type=1)[,1] | |
| imp_df <- data.frame(Marker=names(imp),Importance=imp) | |
| imp_df <- imp_df[imp_df$Importance>0,] | |
| if (nrow(imp_df)==0) return(data.frame(Message="No important markers")) | |
| imp_df <- merge(imp_df,marker_info,by="Marker",all.x=TRUE) | |
| imp_df <- imp_df[!is.na(imp_df$Position),] | |
| max_imp <- max(imp_df$Importance); imp_df$LOD <- imp_df$Importance/max_imp*10 | |
| r2_vec <- sapply(imp_df$Marker,function(m){ | |
| g<-X_v[,m];v<-!is.na(g);if(sum(v)<5) return(NA) | |
| summary(lm(y_v[v]~g[v]))$r.squared*100}) | |
| imp_df$PVE_Marker_Percent<-r2_vec; imp_df$PVE_CIM_Percent<-r2_vec | |
| imp_df$Additive_Effect<-NA; imp_df$P_Value<-10^(-imp_df$LOD) | |
| imp_df$SE_Additive<-NA; imp_df$CI_Lower<-NA; imp_df$CI_Upper<-NA | |
| imp_df$Dominance_Effect<-NA; imp_df$Dominance_Ratio<-NA | |
| imp_df$Dominance_Class<-"Additive"; imp_df$N_Cofactors<-0 | |
| imp_df$N_Samples<-sum(valid); imp_df$Method<-"RF" | |
| imp_df <- imp_df[order(imp_df$Chromosome,imp_df$Position),] | |
| imp_df[,c("Marker","Chromosome","Position","LOD","P_Value","PVE_Marker_Percent","PVE_CIM_Percent", | |
| "Additive_Effect","SE_Additive","CI_Lower","CI_Upper","Dominance_Effect", | |
| "Dominance_Ratio","Dominance_Class","N_Cofactors","N_Samples","Method")] | |
| } | |
| # ============================================================ | |
| # SVR | |
| # ============================================================ | |
| qtl_scan_svr <- function(X, y, marker_info) { | |
| valid <- !is.na(y)&apply(X,1,function(r) !any(is.na(r))) | |
| X_v <- X[valid,,drop=FALSE]; y_v <- y[valid] | |
| svr_fit <- tryCatch(svm(x=X_v,y=y_v,kernel="radial",scale=TRUE),error=function(e) NULL) | |
| if (is.null(svr_fit)) return(data.frame(Message="SVR failed")) | |
| baseline_pred <- predict(svr_fit,X_v) | |
| baseline_error <- mean((y_v-baseline_pred)^2) | |
| importance <- sapply(colnames(X_v),function(m){ | |
| X_perm <- X_v; X_perm[,m] <- sample(X_perm[,m]) | |
| perm_pred <- predict(svr_fit,X_perm) | |
| perm_error <- mean((y_v-perm_pred)^2) | |
| (perm_error-baseline_error)/baseline_error}) | |
| imp_df <- data.frame(Marker=names(importance),Importance=importance) | |
| imp_df <- imp_df[imp_df$Importance>0,] | |
| if (nrow(imp_df)==0) return(data.frame(Message="No important markers")) | |
| imp_df <- merge(imp_df,marker_info,by="Marker",all.x=TRUE) | |
| imp_df <- imp_df[!is.na(imp_df$Position),] | |
| max_imp <- max(imp_df$Importance); imp_df$LOD <- imp_df$Importance/max_imp*10 | |
| r2_vec <- sapply(imp_df$Marker,function(m){ | |
| g<-X_v[,m];v<-!is.na(g);if(sum(v)<5) return(NA) | |
| summary(lm(y_v[v]~g[v]))$r.squared*100}) | |
| imp_df$PVE_Marker_Percent<-r2_vec; imp_df$PVE_CIM_Percent<-r2_vec | |
| imp_df$Additive_Effect<-NA; imp_df$P_Value<-10^(-imp_df$LOD) | |
| imp_df$SE_Additive<-NA; imp_df$CI_Lower<-NA; imp_df$CI_Upper<-NA | |
| imp_df$Dominance_Effect<-NA; imp_df$Dominance_Ratio<-NA | |
| imp_df$Dominance_Class<-"Additive"; imp_df$N_Cofactors<-0 | |
| imp_df$N_Samples<-sum(valid); imp_df$Method<-"SVR" | |
| imp_df <- imp_df[order(imp_df$Chromosome,imp_df$Position),] | |
| imp_df[,c("Marker","Chromosome","Position","LOD","P_Value","PVE_Marker_Percent","PVE_CIM_Percent", | |
| "Additive_Effect","SE_Additive","CI_Lower","CI_Upper","Dominance_Effect", | |
| "Dominance_Ratio","Dominance_Class","N_Cofactors","N_Samples","Method")] | |
| } | |
| # ============================================================ | |
| # XGBOOST | |
| # ============================================================ | |
| qtl_scan_xgboost <- function(X, y, marker_info) { | |
| valid <- !is.na(y)&apply(X,1,function(r) !any(is.na(r))) | |
| X_v <- X[valid,,drop=FALSE]; y_v <- y[valid] | |
| dtrain <- xgb.DMatrix(data=as.matrix(X_v),label=y_v) | |
| xgb_fit <- tryCatch(xgb.train( | |
| data=dtrain,nrounds=100,objective="reg:squarederror",verbose=0, | |
| params=list(max_depth=3,eta=0.1,subsample=0.8,colsample_bytree=0.8)), | |
| error=function(e) NULL) | |
| if (is.null(xgb_fit)) return(data.frame(Message="XGBoost failed")) | |
| importance_matrix <- xgb.importance(model=xgb_fit) | |
| imp_df <- data.frame(Marker=importance_matrix$Feature,Importance=importance_matrix$Gain) | |
| imp_df <- imp_df[imp_df$Importance>0,] | |
| if (nrow(imp_df)==0) return(data.frame(Message="No important markers")) | |
| imp_df <- merge(imp_df,marker_info,by="Marker",all.x=TRUE) | |
| imp_df <- imp_df[!is.na(imp_df$Position),] | |
| max_imp <- max(imp_df$Importance); imp_df$LOD <- imp_df$Importance/max_imp*12 | |
| r2_vec <- sapply(imp_df$Marker,function(m){ | |
| g<-X_v[,m];v<-!is.na(g);if(sum(v)<5) return(NA) | |
| summary(lm(y_v[v]~g[v]))$r.squared*100}) | |
| imp_df$PVE_Marker_Percent<-r2_vec; imp_df$PVE_CIM_Percent<-r2_vec | |
| imp_df$Additive_Effect<-NA; imp_df$P_Value<-10^(-imp_df$LOD) | |
| imp_df$SE_Additive<-NA; imp_df$CI_Lower<-NA; imp_df$CI_Upper<-NA | |
| imp_df$Dominance_Effect<-NA; imp_df$Dominance_Ratio<-NA | |
| imp_df$Dominance_Class<-"Additive"; imp_df$N_Cofactors<-0 | |
| imp_df$N_Samples<-sum(valid); imp_df$Method<-"XGBOOST" | |
| imp_df <- imp_df[order(imp_df$Chromosome,imp_df$Position),] | |
| imp_df[,c("Marker","Chromosome","Position","LOD","P_Value","PVE_Marker_Percent","PVE_CIM_Percent", | |
| "Additive_Effect","SE_Additive","CI_Lower","CI_Upper","Dominance_Effect", | |
| "Dominance_Ratio","Dominance_Class","N_Cofactors","N_Samples","Method")] | |
| } | |
| # ============================================================ | |
| # MAIN DISPATCHER | |
| # ============================================================ | |
| qtl_scan <- function(genotype_matrix, phenotype_vector, marker_info, | |
| method="CIM", n_cofactors=10, window_size=10) { | |
| cat("\n=== QTL SCAN === Method:", method, "\n") | |
| valid_rows <- !is.na(phenotype_vector) | |
| X_valid <- genotype_matrix[valid_rows,,drop=FALSE] | |
| y_valid <- phenotype_vector[valid_rows] | |
| nzv <- apply(X_valid, 2, function(x) length(unique(na.omit(x))) < 2) | |
| X_valid <- X_valid[, !nzv, drop=FALSE] | |
| cat("Markers after NZV removal:", ncol(X_valid), "\n") | |
| if (method == "SIM") return(qtl_scan_sim(X_valid, y_valid, marker_info)) | |
| if (method == "CIM") return(qtl_scan_cim_improved(X_valid, y_valid, marker_info, window_size, n_cofactors)) | |
| if (method == "MQM") return(qtl_scan_mqm(X_valid, y_valid, marker_info)) | |
| if (method == "RF") return(qtl_scan_rf(X_valid, y_valid, marker_info)) | |
| if (method == "SVR") return(qtl_scan_svr(X_valid, y_valid, marker_info)) | |
| if (method == "XGBOOST") return(qtl_scan_xgboost(X_valid, y_valid, marker_info)) | |
| data.frame(Message=paste("Unknown method:", method)) | |
| } | |
| # ============================================================ | |
| # LOD SUPPORT INTERVAL | |
| # ============================================================ | |
| add_lod_support_interval <- function(sig_df, all_df, lod_drop=1.5) { | |
| sig_df$Low_Flank <- sig_df$High_Flank <- NA_character_ | |
| sig_df$Interval_Start <- sig_df$Interval_End <- sig_df$Interval_Size_cM <- NA_real_ | |
| for (i in 1:nrow(sig_df)) { | |
| m <- sig_df$Marker[i]; ch <- sig_df$Chromosome[i] | |
| peak_lod <- sig_df$LOD[i]; peak_pos <- sig_df$Position[i] | |
| chr_data <- all_df[as.character(all_df$Chromosome)==as.character(ch)&!is.na(all_df$LOD)&!is.na(all_df$Position),] | |
| chr_data <- chr_data[order(chr_data$Position),] | |
| if (nrow(chr_data)==0) next | |
| threshold <- peak_lod - lod_drop | |
| interval_markers <- chr_data[chr_data$LOD >= threshold,] | |
| if (nrow(interval_markers) > 0) { | |
| interval_start <- min(interval_markers$Position, peak_pos) | |
| interval_end <- max(interval_markers$Position, peak_pos) | |
| sig_df$Interval_Start[i] <- interval_start | |
| sig_df$Interval_End[i] <- interval_end | |
| sig_df$Interval_Size_cM[i] <- interval_end - interval_start | |
| below_markers <- chr_data[chr_data$Position < interval_start,] | |
| above_markers <- chr_data[chr_data$Position > interval_end,] | |
| sig_df$Low_Flank[i] <- if(nrow(below_markers)>0) below_markers$Marker[nrow(below_markers)] else m | |
| sig_df$High_Flank[i] <- if(nrow(above_markers)>0) above_markers$Marker[1] else m | |
| } else { | |
| peak_idx <- which(chr_data$Marker==m) | |
| if (length(peak_idx)>0) { | |
| sig_df$Low_Flank[i] <- if(peak_idx>1) chr_data$Marker[peak_idx-1] else m | |
| sig_df$High_Flank[i] <- if(peak_idx<nrow(chr_data)) chr_data$Marker[peak_idx+1] else m | |
| sig_df$Interval_Start[i] <- sig_df$Position[i] | |
| sig_df$Interval_End[i] <- sig_df$Position[i] | |
| sig_df$Interval_Size_cM[i] <- 0 | |
| } | |
| } | |
| } | |
| sig_df | |
| } | |
| # ============================================================ | |
| # CHROMOSOME IDEOGRAM — NOW ACCEPTS COLOR & SIZE PARAMS | |
| # ============================================================ | |
| make_chromosome_ideogram_reference <- function(qtl_list, all_markers_df, | |
| lod_threshold = 2.0, | |
| model_colors = MODEL_COLORS, | |
| title_str = NULL, | |
| band_dark_col = "#2E6B35", | |
| band_light_col = "#6AAB6E", | |
| border_col = "#1A4A1F", | |
| qtl_size_min = 0.7, | |
| qtl_size_max = 2.0) { | |
| if (is.null(all_markers_df) || nrow(all_markers_df) == 0) return(invisible(NULL)) | |
| chroms <- sort(unique(as.character(all_markers_df$Chromosome))) | |
| n_chr <- length(chroms) | |
| x_gap <- 1.8 | |
| bar_w <- 0.55 | |
| max_pos <- max(all_markers_df$Position, na.rm=TRUE) | |
| model_names <- names(qtl_list) | |
| par(mar=c(5, 4, 4, 8), bg="white", xpd=TRUE) | |
| plot(0, 0, type="n", | |
| xlim=c(0.2, n_chr * x_gap + 0.3), | |
| ylim=c(-8, max_pos * 1.12), | |
| xaxt="n", yaxt="n", xlab="", ylab="", main="", frame.plot=FALSE, bty="n") | |
| y_ticks <- pretty(c(0, max_pos), n=7) | |
| axis(2, at=y_ticks, labels=y_ticks, cex.axis=0.72, las=1, | |
| col="#555555", col.axis="#333333", tcl=-0.3, lwd=0.8) | |
| mtext("Genetic Position (cM)", side=2, line=2.5, cex=0.82, col="#222222", font=2) | |
| for (ci in seq_along(chroms)) { | |
| chr <- chroms[ci] | |
| x_center <- (ci - 1) * x_gap + 0.9 | |
| chr_m <- all_markers_df[as.character(all_markers_df$Chromosome)==chr, ] | |
| if (nrow(chr_m)==0) next | |
| chr_len <- max(chr_m$Position, na.rm=TRUE) | |
| chr_min <- min(chr_m$Position, na.rm=TRUE) | |
| n_bands <- max(8, round((chr_len - chr_min) / 8)) | |
| band_h <- (chr_len - chr_min) / n_bands | |
| for (bi in 1:n_bands) { | |
| band_bot <- chr_min + (bi-1)*band_h | |
| band_top <- chr_min + bi*band_h | |
| bcol <- if (bi %% 2 == 1) band_dark_col else band_light_col | |
| rect(x_center - bar_w/2, band_bot, x_center + bar_w/2, band_top, | |
| col=bcol, border=NA) | |
| } | |
| rect(x_center - bar_w/2, chr_min, x_center + bar_w/2, chr_len, | |
| col=NA, border=border_col, lwd=1.8) | |
| symbols(x_center, chr_min, circles=bar_w/2, inches=FALSE, | |
| add=TRUE, fg=border_col, bg=band_light_col, lwd=1.2) | |
| symbols(x_center, chr_len, circles=bar_w/2, inches=FALSE, | |
| add=TRUE, fg=border_col, bg=band_dark_col, lwd=1.2) | |
| cen_pos <- chr_min + (chr_len - chr_min)*0.42 | |
| polygon(c(x_center-bar_w/2, x_center, x_center+bar_w/2, x_center), | |
| c(cen_pos - chr_len*0.025, cen_pos, | |
| cen_pos - chr_len*0.025, cen_pos - chr_len*0.05), | |
| col="white", border=border_col, lwd=1.2) | |
| polygon(c(x_center-bar_w/2, x_center, x_center+bar_w/2, x_center), | |
| c(cen_pos + chr_len*0.025, cen_pos, | |
| cen_pos + chr_len*0.025, cen_pos + chr_len*0.05), | |
| col="white", border=border_col, lwd=1.2) | |
| n_models <- length(model_names) | |
| for (ti in seq_along(model_names)) { | |
| trait <- model_names[ti] | |
| qtl_data <- qtl_list[[trait]] | |
| if (is.null(qtl_data) || "Message" %in% colnames(qtl_data)) next | |
| sig_qtls <- qtl_data[as.character(qtl_data$Chromosome)==chr & | |
| !is.na(qtl_data$LOD) & | |
| qtl_data$LOD >= lod_threshold, ] | |
| if (nrow(sig_qtls)==0) next | |
| col_m <- model_colors[trait] | |
| if (is.na(col_m)) col_m <- "#CC0000" | |
| x_off <- x_center + bar_w/2 + 0.08 + (ti-1)*0.22 | |
| for (ri in 1:nrow(sig_qtls)) { | |
| pos <- sig_qtls$Position[ri] | |
| lod_val <- sig_qtls$LOD[ri] | |
| segments(x_center + bar_w/2, pos, x_off - 0.04, pos, | |
| col=col_m, lwd=1.5) | |
| points(x_off, pos, pch=16, col=col_m, | |
| cex=pmax(qtl_size_min, pmin(qtl_size_max, 0.6 + lod_val/8))) | |
| text(x_off + 0.15, pos, | |
| paste0(round(lod_val,1)), | |
| cex=0.55, col=col_m, font=2, adj=0) | |
| } | |
| } | |
| text(x_center, chr_min - max_pos*0.04, paste0("Chr", chr), | |
| cex=0.72, font=2, col="#1A1A1A", adj=0.5) | |
| } | |
| legend_x <- n_chr * x_gap + 0.5 | |
| legend_y <- max_pos * 0.95 | |
| text(legend_x, legend_y + max_pos*0.07, "Models", cex=0.75, font=2, col="#1A1A1A", adj=0) | |
| for (ti in seq_along(model_names)) { | |
| tr <- model_names[ti] | |
| col_m <- model_colors[tr]; if (is.na(col_m)) col_m <- "#CC0000" | |
| y_leg <- legend_y - (ti-1)*max_pos*0.07 | |
| qtl_d <- qtl_list[[tr]] | |
| n_sig <- if(!is.null(qtl_d)&&!"Message"%in%colnames(qtl_d)) | |
| sum(!is.na(qtl_d$LOD)&qtl_d$LOD>=lod_threshold,na.rm=TRUE) else 0 | |
| points(legend_x, y_leg, pch=16, col=col_m, cex=1.2) | |
| text(legend_x + 0.18, y_leg, | |
| paste0(tr, " (", n_sig, " QTL)"), | |
| cex=0.65, col=col_m, font=1, adj=0) | |
| } | |
| ttl <- if (!is.null(title_str)) title_str else paste0("QTL Chromosome Map | LOD ≥ ", lod_threshold) | |
| title(main=ttl, cex.main=0.95, font.main=2, col.main="#1A1A1A", line=2) | |
| } | |
| # ============================================================ | |
| # CHROMOSOME-WISE MANHATTAN PLOT (PLOTLY) — FULLY CUSTOMIZABLE | |
| # ============================================================ | |
| make_plotly_manhattan_chromosomewise <- function(scan_all, lod_threshold=2.0, | |
| title="QTL Manhattan Plot", | |
| nonsig_color="#3A7D44", | |
| sig_color="#CC0000", | |
| point_size=6, | |
| sig_point_size=10, | |
| threshold_line_width=1.5, | |
| threshold_line_style="dash") { | |
| blank <- plot_ly() %>% layout(title=title, plot_bgcolor="white", paper_bgcolor="white") | |
| if (is.null(scan_all) || "Message" %in% colnames(scan_all)) return(blank) | |
| pd <- scan_all[!is.na(scan_all$LOD) & !is.na(scan_all$Position) & !is.na(scan_all$Chromosome), ] | |
| if (nrow(pd) == 0) return(blank) | |
| pd$Chromosome <- as.character(pd$Chromosome) | |
| pd$sig <- pd$LOD >= lod_threshold | |
| chroms <- sort(unique(pd$Chromosome)) | |
| subplot_list <- list() | |
| for (chr in chroms) { | |
| chr_data <- pd[pd$Chromosome == chr, ] | |
| chr_data <- chr_data[order(chr_data$Position), ] | |
| nonsig <- chr_data[!chr_data$sig, ] | |
| sig_data <- chr_data[chr_data$sig, ] | |
| p <- plot_ly() | |
| if (nrow(nonsig) > 0) { | |
| p <- p %>% add_trace( | |
| data = nonsig, x = ~Position, y = ~LOD, | |
| type = "scatter", mode = "markers", | |
| marker = list(size = point_size, color = nonsig_color, opacity = 0.7), | |
| text = ~paste0("<b>", Marker, "</b><br>Chr: ", Chromosome, | |
| "<br>Pos: ", round(Position, 1), " cM<br>LOD: ", round(LOD, 2), | |
| "<br>PVE: ", round(PVE_Marker_Percent, 1), "%"), | |
| hoverinfo = "text", showlegend = FALSE, name = paste("Chr", chr) | |
| ) | |
| } | |
| if (nrow(sig_data) > 0) { | |
| p <- p %>% add_trace( | |
| data = sig_data, x = ~Position, y = ~LOD, | |
| type = "scatter", mode = "markers", | |
| marker = list(size = sig_point_size, color = sig_color, | |
| symbol = "diamond", line = list(width = 1, color = "#660000")), | |
| text = ~paste0("<b>", Marker, "</b><br>Chr: ", Chromosome, | |
| "<br>Pos: ", round(Position, 1), " cM<br>LOD: ", round(LOD, 2), | |
| "<br>PVE: ", round(PVE_Marker_Percent, 1), "%"), | |
| hoverinfo = "text", showlegend = FALSE, name = paste("Chr", chr, "Significant") | |
| ) | |
| } | |
| p <- p %>% add_segments( | |
| x = min(chr_data$Position, na.rm=TRUE), xend = max(chr_data$Position, na.rm=TRUE), | |
| y = lod_threshold, yend = lod_threshold, | |
| line = list(color = sig_color, dash = threshold_line_style, width = threshold_line_width), | |
| showlegend = FALSE, hoverinfo = "none" | |
| ) | |
| p <- p %>% layout( | |
| xaxis = list(title = paste("Chr", chr, "Position (cM)"), gridcolor = "#EEEEEE", zeroline = FALSE), | |
| yaxis = list(title = if(chr == chroms[1]) "LOD Score" else "", gridcolor = "#EEEEEE", zeroline = FALSE), | |
| plot_bgcolor = "white", paper_bgcolor = "white" | |
| ) | |
| subplot_list[[chr]] <- p | |
| } | |
| n_chroms <- length(chroms) | |
| n_cols <- ceiling(sqrt(n_chroms)) | |
| n_rows <- ceiling(n_chroms / n_cols) | |
| combined_plot <- subplot( | |
| subplot_list, nrows = n_rows, shareY = TRUE, | |
| titleX = TRUE, titleY = TRUE, margin = 0.05 | |
| ) | |
| combined_plot %>% layout( | |
| title = list( | |
| text = paste0(title, " | ", sum(pd$sig, na.rm=TRUE), " significant QTLs"), | |
| font = list(size = 14, color = "#111111") | |
| ), | |
| showlegend = FALSE, | |
| font = list(color = "#111111"), | |
| hoverlabel = list(bgcolor = "#FFFFFF", font = list(color = "#111111", size = 11)) | |
| ) | |
| } | |
| # ============================================================ | |
| # COMMON QTL FINDER | |
| # ============================================================ | |
| find_common_qtl <- function(compare_res, lod_threshold, min_models=2, window_cM=5) { | |
| sig_list <- list() | |
| for (mth in names(compare_res)) { | |
| r <- compare_res[[mth]] | |
| if (is.null(r)||"Message"%in%colnames(r)) next | |
| sig <- r[!is.na(r$LOD)&r$LOD>=lod_threshold, | |
| c("Marker","Chromosome","Position","LOD","PVE_Marker_Percent","Additive_Effect"),drop=FALSE] | |
| if (nrow(sig)>0){sig$Model<-mth;sig_list[[mth]]<-sig} | |
| } | |
| if (length(sig_list)==0) return(NULL) | |
| all_sig <- do.call(rbind,sig_list) | |
| all_sig$Chromosome <- as.character(all_sig$Chromosome) | |
| marker_counts <- table(all_sig$Marker) | |
| all_sig$N_Models_Detected <- as.integer(marker_counts[all_sig$Marker]) | |
| common <- all_sig[all_sig$N_Models_Detected>=min_models,] | |
| if (nrow(common)==0) return(NULL) | |
| markers_uniq <- unique(common$Marker) | |
| summary_rows <- lapply(markers_uniq, function(mk) { | |
| rows <- common[common$Marker==mk,] | |
| base <- rows[which.max(rows$LOD), c("Marker","Chromosome","Position","N_Models_Detected")] | |
| base$Models_List <- paste(sort(unique(rows$Model)), collapse=", ") | |
| base$Mean_LOD <- round(mean(rows$LOD, na.rm=TRUE), 3) | |
| base$Max_LOD <- round(max(rows$LOD, na.rm=TRUE), 3) | |
| base$Mean_PVE <- round(mean(rows$PVE_Marker_Percent, na.rm=TRUE), 3) | |
| base$Mean_Add_Eff <- round(mean(rows$Additive_Effect, na.rm=TRUE), 3) | |
| for (mth in names(compare_res)) { | |
| r_mth <- rows[rows$Model==mth,] | |
| base[[paste0("LOD_",mth)]] <- if(nrow(r_mth)>0) round(r_mth$LOD[1],3) else NA | |
| } | |
| base | |
| }) | |
| result <- do.call(rbind, summary_rows) | |
| result[order(-result$N_Models_Detected, -result$Max_LOD),] | |
| } | |
| # ============================================================ | |
| # UI | |
| # ============================================================ | |
| ui <- fluidPage( | |
| shinyjs::useShinyjs(), | |
| tags$head( | |
| tags$link(rel="stylesheet", | |
| href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap"), | |
| tags$style(HTML(" | |
| *{box-sizing:border-box;} | |
| body{font-family:'Inter',sans-serif;background:#F7F7F5;color:#1A1A1A;font-size:14px;margin:0;} | |
| .navbar{background:#1A4A1F!important;border-bottom:none!important;} | |
| .navbar .navbar-brand,.navbar-default .navbar-brand{color:#FFFFFF!important;font-weight:700;} | |
| .navbar-default .navbar-nav>li>a{color:#D0E8D0!important;} | |
| .navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:hover{ | |
| background:#2E6B35!important;color:#FFFFFF!important;} | |
| .navbar-default .navbar-nav>li>a:hover{background:#2E6B35!important;color:#FFFFFF!important;} | |
| .card{background:#FFFFFF;border:1px solid #DDDDDD;border-radius:10px; | |
| margin-bottom:16px;box-shadow:0 1px 4px rgba(0,0,0,0.06);} | |
| .card-body{padding:16px;} | |
| .info-box{background:#EAF4EA;border:1px solid #A8D4A8;border-left:4px solid #2E6B35; | |
| border-radius:6px;padding:10px 14px;margin:8px 0;font-size:13px;color:#1A4A1F;} | |
| .btn-primary{background:#2E6B35!important;border-color:#1A4A1F!important;color:#FFFFFF!important;} | |
| .btn-primary:hover{background:#1A4A1F!important;} | |
| .btn-success{background:#3A7D44!important;border-color:#2E6B35!important;color:#FFFFFF!important;} | |
| .btn-info{background:#4A8FA0!important;border-color:#2E6B35!important;color:#FFFFFF!important;} | |
| .btn-warning{background:#C8760A!important;border-color:#A05E05!important;color:#FFFFFF!important;} | |
| .btn-danger{background:#990000!important;border-color:#660000!important;color:#FFFFFF!important;} | |
| .download-row{padding:10px 0 6px 0;display:flex;flex-wrap:wrap;gap:8px;} | |
| label{font-weight:500;color:#1A1A1A;} | |
| .shiny-input-container{margin-bottom:12px;} | |
| h4{color:#1A4A1F;font-weight:600;} | |
| .section-title{font-size:13px;font-weight:600;color:#2E6B35;margin-bottom:6px; | |
| border-bottom:1px solid #CCDDCC;padding-bottom:4px;} | |
| .custom-section{background:#F9FBFA;padding:12px;border-radius:6px;margin:8px 0; | |
| border:1px solid #D5E8D5;} | |
| ")) | |
| ), | |
| navbarPage( | |
| title = "🌿 QTL-Mapper v7 Enhanced", | |
| id = "nav", | |
| # ── DATA UPLOAD ───────────────────────────────────────── | |
| tabPanel("📁 Data Upload", | |
| fluidRow( | |
| column(4, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","📂 Input Files"), | |
| fileInput("geno_file","Genotype CSV",accept=".csv", | |
| placeholder="CSV: row1=header, row2=chr, row3=pos"), | |
| fileInput("pheno_file","Phenotype CSV",accept=".csv"), | |
| selectInput("pheno_col","Trait / Phenotype",choices=NULL) | |
| )) | |
| ), | |
| column(4, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","⚙️ Analysis Settings"), | |
| selectInput("mapping_method","Mapping Method", | |
| choices=setNames(names(MAPPING_MODELS),sapply(MAPPING_MODELS,`[[`,"label")), | |
| selected="CIM"), | |
| sliderInput("lod_threshold","LOD Threshold",min=0.5,max=10,value=2.5,step=0.1), | |
| sliderInput("val_prop","Validation Fraction",min=0.1,max=0.4,value=0.2,step=0.05), | |
| conditionalPanel("input.mapping_method == 'CIM'", | |
| numericInput("n_cofactors","Cofactors (K)",value=5,min=1,max=20), | |
| numericInput("window_size","Window (± markers)",value=10,min=3,max=30) | |
| ), | |
| numericInput("lod_drop","LOD Drop (support interval)",value=1.0,min=0.3,max=2.0,step=0.1), | |
| br(), | |
| actionButton("run_btn","▶ Run Analysis",class="btn btn-primary",style="width:100%;") | |
| )) | |
| ), | |
| column(4, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","📊 Dataset Info"), | |
| uiOutput("dataset_info_ui"), | |
| br(), | |
| div(class="info-box", | |
| "6 models: SIM · CIM · MQM · RF · SVR · XGBoost",br(), | |
| "Chromosome-wise Manhattan plots (no marker labels)",br(), | |
| "Full plot customization available in Plot Settings tab" | |
| ) | |
| )) | |
| ) | |
| ) | |
| ), | |
| # ── PLOT CUSTOMIZATION ────────────────────────────────── | |
| tabPanel("🎨 Plot Settings", | |
| fluidRow( | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","📊 Manhattan Plot Customization"), | |
| div(class="custom-section", | |
| h5("📐 Plot Dimensions"), | |
| sliderInput("manhattan_height","Plot Height (pixels)",min=400,max=1200,value=800,step=50), | |
| sliderInput("manhattan_width","Plot Width (pixels)",min=800,max=2000,value=1400,step=100) | |
| ), | |
| div(class="custom-section", | |
| h5("🎯 Point Styling"), | |
| sliderInput("nonsig_point_size","Non-Significant Point Size",min=2,max=12,value=6,step=1), | |
| sliderInput("sig_point_size","Significant Point Size",min=4,max=20,value=10,step=1), | |
| colourpicker::colourInput("nonsig_color","Non-Significant Color","#3A7D44"), | |
| colourpicker::colourInput("sig_color","Significant Color","#CC0000") | |
| ), | |
| div(class="custom-section", | |
| h5("📏 Threshold Line"), | |
| sliderInput("threshold_width","Threshold Line Width",min=0.5,max=4,value=1.5,step=0.5), | |
| selectInput("threshold_style","Threshold Line Style", | |
| choices=c("Solid"="solid","Dashed"="dash","Dotted"="dot","Dash-Dot"="dashdot"), | |
| selected="dash") | |
| ) | |
| )) | |
| ), | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","🗺 Ideogram Plot Customization"), | |
| div(class="custom-section", | |
| h5("📐 Plot Dimensions"), | |
| sliderInput("ideogram_height","Plot Height (pixels)",min=400,max=1200,value=900,step=50), | |
| sliderInput("ideogram_width","Plot Width (pixels)",min=800,max=2000,value=1400,step=100) | |
| ), | |
| div(class="custom-section", | |
| h5("🎨 Chromosome Colors"), | |
| colourpicker::colourInput("chr_dark","Dark Band Color","#2E6B35"), | |
| colourpicker::colourInput("chr_light","Light Band Color","#6AAB6E"), | |
| colourpicker::colourInput("chr_border","Border Color","#1A4A1F") | |
| ), | |
| div(class="custom-section", | |
| h5("🔴 QTL Marker Sizing"), | |
| sliderInput("qtl_marker_size_min","Min Marker Size",min=0.3,max=1.5,value=0.7,step=0.1), | |
| sliderInput("qtl_marker_size_max","Max Marker Size",min=1.0,max=3.0,value=2.0,step=0.1) | |
| ) | |
| )) | |
| ) | |
| ) | |
| ), | |
| # ── RESULTS ───────────────────────────────────────────── | |
| tabPanel("📊 Results", | |
| div(class="download-row", | |
| downloadButton("dl_manhattan_png","🖼 Manhattan (PNG)",class="btn btn-info"), | |
| downloadButton("dl_idiogram_png","🗺 Ideogram (PNG)",class="btn btn-info"), | |
| downloadButton("dl_qtl_csv","📥 QTL Table (CSV)",class="btn btn-success"), | |
| downloadButton("dl_full_csv","📥 Full Scan (CSV)",class="btn btn-warning") | |
| ), | |
| fluidRow( | |
| column(12, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","📊 Chromosome-wise Manhattan Plot"), | |
| # Dynamic height driven by slider via uiOutput | |
| uiOutput("manhattan_plot_ui") | |
| )) | |
| ) | |
| ), | |
| fluidRow( | |
| column(12, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","🗺 Chromosome Ideogram"), | |
| uiOutput("idiogram_plot_ui") | |
| )) | |
| ) | |
| ), | |
| fluidRow(column(12, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","📋 Significant QTL Table"), | |
| DTOutput("qtl_table") | |
| )) | |
| )) | |
| ), | |
| # ── COMPARE MODELS ─────────────────────────────────────── | |
| tabPanel("⚖️ Compare Models", | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Select Models to Compare"), | |
| checkboxGroupInput("compare_methods","", | |
| choices=setNames(names(MAPPING_MODELS),sapply(MAPPING_MODELS,`[[`,"label")), | |
| selected=c("SIM","CIM","MQM"),inline=TRUE), | |
| actionButton("run_compare","🚀 Run Comparison",class="btn btn-primary"), | |
| uiOutput("compare_status_ui") | |
| )), | |
| div(class="download-row", | |
| downloadButton("dl_compare_idiogram_png","🖼 Compare Ideogram (PNG)",class="btn btn-info"), | |
| downloadButton("dl_compare_bar_png","📊 Compare Bar (PNG)",class="btn btn-info"), | |
| downloadButton("dl_compare_csv","📥 Compare Table (CSV)",class="btn btn-success") | |
| ), | |
| fluidRow( | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Multi-Model Chromosome Ideogram"), | |
| plotOutput("compare_ideogram",height="520px") | |
| )) | |
| ), | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","QTL Counts per Model"), | |
| plotlyOutput("compare_bar",height="520px") | |
| )) | |
| ) | |
| ), | |
| fluidRow(column(12, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Full Comparison Table"), | |
| DTOutput("compare_table") | |
| )) | |
| )) | |
| ), | |
| # ── COMMON QTL ─────────────────────────────────────────── | |
| tabPanel("🎯 Common QTL", | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Common QTL Across Models"), | |
| sliderInput("min_models","Minimum Models Detecting QTL",min=2,max=6,value=2,step=1,width="400px"), | |
| uiOutput("common_qtl_summary_ui") | |
| )), | |
| div(class="download-row", | |
| downloadButton("dl_common_idiogram_png","🖼 Common Ideogram (PNG)",class="btn btn-info"), | |
| downloadButton("dl_common_csv","📥 Common QTL (CSV)",class="btn btn-success") | |
| ), | |
| fluidRow( | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Common QTL Ideogram"), | |
| plotOutput("common_ideogram",height="500px") | |
| )) | |
| ), | |
| column(6, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Common QTL Counts"), | |
| plotlyOutput("common_bar",height="500px") | |
| )) | |
| ) | |
| ), | |
| fluidRow(column(12, | |
| div(class="card", div(class="card-body", | |
| div(class="section-title","Common QTL Table"), | |
| DTOutput("common_qtl_table") | |
| )) | |
| )) | |
| ) | |
| ) | |
| ) | |
| # ============================================================ | |
| # SERVER | |
| # ============================================================ | |
| server <- function(input, output, session) { | |
| rv <- reactiveValues( | |
| geno=NULL, pheno=NULL, marker_info=NULL, all_markers=NULL, | |
| scan_all=NULL, compare_res=list() | |
| ) | |
| lod_thr <- reactive(input$lod_threshold) | |
| # ── Safe color helpers (with fallbacks) ── | |
| col_nonsig <- reactive(get_color(input$nonsig_color, "#3A7D44")) | |
| col_sig <- reactive(get_color(input$sig_color, "#CC0000")) | |
| col_chr_dark <- reactive(get_color(input$chr_dark, "#2E6B35")) | |
| col_chr_light <- reactive(get_color(input$chr_light, "#6AAB6E")) | |
| col_chr_border <- reactive(get_color(input$chr_border, "#1A4A1F")) | |
| # ── DATASET INFO ── | |
| output$dataset_info_ui <- renderUI({ | |
| if (is.null(rv$geno)) return(div(class="info-box","📂 No genotype file loaded")) | |
| tagList( | |
| div(class="info-box", | |
| tags$b(nrow(rv$geno)), " Samples · ", | |
| tags$b(ncol(rv$geno)), " Markers · ", | |
| tags$b(length(unique(rv$marker_info$Chromosome))), " Chromosomes" | |
| ) | |
| ) | |
| }) | |
| # ── LOAD GENOTYPE ── | |
| observeEvent(input$geno_file, { | |
| req(input$geno_file) | |
| tryCatch({ | |
| res <- load_genotype_file(input$geno_file$datapath) | |
| rv$geno <- res$geno; rv$marker_info <- res$marker_info; rv$all_markers <- res$marker_info | |
| showNotification(paste("✅ Loaded",res$n_markers,"markers,", | |
| length(res$chromosomes),"chromosomes"),type="message") | |
| }, error=function(e) showNotification(paste("❌",e$message),type="error")) | |
| }) | |
| # ── LOAD PHENOTYPE ── | |
| observeEvent(input$pheno_file, { | |
| req(input$pheno_file) | |
| tryCatch({ | |
| pd <- read.csv(input$pheno_file$datapath) | |
| rownames(pd) <- pd[,1]; pd <- pd[,-1,drop=FALSE] | |
| for (col in colnames(pd)) pd[[col]] <- as.numeric(as.character(pd[[col]])) | |
| rv$pheno <- pd | |
| updateSelectInput(session,"pheno_col",choices=colnames(pd),selected=colnames(pd)[1]) | |
| showNotification(paste("✅ Loaded",ncol(pd),"traits"),type="message") | |
| }, error=function(e) showNotification(paste("❌",e$message),type="error")) | |
| }) | |
| # ── RUN ANALYSIS ── | |
| observeEvent(input$run_btn, { | |
| req(rv$geno, rv$pheno, input$pheno_col) | |
| showNotification(paste("🚀 Running",input$mapping_method,"..."),type="message",duration=5) | |
| tryCatch({ | |
| cs <- intersect(rownames(rv$geno),rownames(rv$pheno)) | |
| X <- rv$geno[cs,,drop=FALSE] | |
| y <- as.numeric(rv$pheno[cs,input$pheno_col]) | |
| set.seed(42) | |
| tr_idx <- caret::createDataPartition(y,p=1-input$val_prop,list=FALSE) | |
| X_tr <- X[tr_idx,,drop=FALSE]; y_tr <- y[tr_idx] | |
| withProgress(message=paste("Scanning genome with",input$mapping_method),value=0.1,{ | |
| rv$scan_all <- qtl_scan(X_tr, y_tr, rv$marker_info, | |
| method=input$mapping_method, | |
| n_cofactors=input$n_cofactors, | |
| window_size=input$window_size) | |
| incProgress(0.9) | |
| }) | |
| n_sig <- if(!is.null(rv$scan_all)&&!"Message"%in%colnames(rv$scan_all)) | |
| sum(rv$scan_all$LOD>=lod_thr()&!is.na(rv$scan_all$LOD),na.rm=TRUE) else 0 | |
| showNotification(paste("✅",n_sig,"significant QTLs detected"),type="message") | |
| updateTabsetPanel(session,"nav",selected="📊 Results") | |
| }, error=function(e) showNotification(paste("❌",e$message),type="error")) | |
| }) | |
| # ── RUN COMPARISON ── | |
| observeEvent(input$run_compare, { | |
| req(rv$geno, rv$pheno, input$compare_methods) | |
| cs <- intersect(rownames(rv$geno),rownames(rv$pheno)) | |
| X <- rv$geno[cs,,drop=FALSE] | |
| y <- as.numeric(rv$pheno[cs,input$pheno_col]) | |
| set.seed(42) | |
| tr_idx <- caret::createDataPartition(y,p=1-input$val_prop,list=FALSE) | |
| X_tr <- X[tr_idx,,drop=FALSE]; y_tr <- y[tr_idx] | |
| compare_list <- list() | |
| withProgress(message="Comparing models",value=0,{ | |
| for (i in seq_along(input$compare_methods)) { | |
| mth <- input$compare_methods[i] | |
| incProgress(1/length(input$compare_methods),detail=mth) | |
| res <- tryCatch(qtl_scan(X_tr, y_tr, rv$marker_info, | |
| method=mth, | |
| n_cofactors=input$n_cofactors, | |
| window_size=input$window_size), | |
| error=function(e) NULL) | |
| if (!is.null(res)) compare_list[[mth]] <- res | |
| } | |
| }) | |
| rv$compare_res <- compare_list | |
| showNotification(paste("✅ Compared",length(compare_list),"models"),type="message") | |
| }) | |
| output$compare_status_ui <- renderUI({ | |
| if (length(rv$compare_res)==0) return(NULL) | |
| div(class="info-box", | |
| tags$b(length(rv$compare_res)), " models compared: ", | |
| paste(names(rv$compare_res),collapse=" · ")) | |
| }) | |
| # ── DYNAMIC UI FOR PLOTS (height/width from sliders) ── | |
| output$manhattan_plot_ui <- renderUI({ | |
| h <- if(!is.null(input$manhattan_height)) input$manhattan_height else 800 | |
| plotlyOutput("manhattan_plot", height=paste0(h,"px")) | |
| }) | |
| output$idiogram_plot_ui <- renderUI({ | |
| h <- if(!is.null(input$ideogram_height)) input$ideogram_height else 900 | |
| plotOutput("idiogram_plot", height=paste0(h,"px")) | |
| }) | |
| # ── MANHATTAN PLOT (interactive, fully customised) ── | |
| output$manhattan_plot <- renderPlotly({ | |
| req(rv$scan_all) | |
| make_plotly_manhattan_chromosomewise( | |
| rv$scan_all, | |
| lod_thr(), | |
| paste(input$mapping_method, "Manhattan — Trait:", input$pheno_col), | |
| nonsig_color = col_nonsig(), | |
| sig_color = col_sig(), | |
| point_size = input$nonsig_point_size, | |
| sig_point_size = input$sig_point_size, | |
| threshold_line_width = input$threshold_width, | |
| threshold_line_style = input$threshold_style | |
| ) | |
| }) | |
| # ── IDEOGRAM PLOT (base R, fully customised) ── | |
| output$idiogram_plot <- renderPlot({ | |
| req(rv$scan_all, rv$all_markers) | |
| res_list <- list() | |
| if (!is.null(rv$scan_all) && !"Message" %in% colnames(rv$scan_all)) | |
| res_list[[input$mapping_method]] <- rv$scan_all | |
| par(bg="white") | |
| make_chromosome_ideogram_reference( | |
| res_list, rv$all_markers, lod_thr(), | |
| title_str = paste(input$mapping_method, "— Trait:", input$pheno_col), | |
| band_dark_col = col_chr_dark(), | |
| band_light_col = col_chr_light(), | |
| border_col = col_chr_border(), | |
| qtl_size_min = input$qtl_marker_size_min, | |
| qtl_size_max = input$qtl_marker_size_max | |
| ) | |
| }, bg="white", | |
| width = function() if(!is.null(input$ideogram_width)) input$ideogram_width else 1400, | |
| height = function() if(!is.null(input$ideogram_height)) input$ideogram_height else 900) | |
| # ── COMPARE IDEOGRAM ── | |
| output$compare_ideogram <- renderPlot({ | |
| req(length(rv$compare_res)>0, rv$all_markers) | |
| par(bg="white") | |
| make_chromosome_ideogram_reference( | |
| rv$compare_res, rv$all_markers, lod_thr(), | |
| title_str = "Multi-Model QTL Comparison", | |
| band_dark_col = col_chr_dark(), | |
| band_light_col = col_chr_light(), | |
| border_col = col_chr_border(), | |
| qtl_size_min = input$qtl_marker_size_min, | |
| qtl_size_max = input$qtl_marker_size_max | |
| ) | |
| }, bg="white") | |
| # ── COMPARE BAR CHART ── | |
| output$compare_bar <- renderPlotly({ | |
| req(length(rv$compare_res)>0) | |
| counts <- sapply(rv$compare_res, function(r) { | |
| if(is.null(r)||"Message"%in%colnames(r)) return(0) | |
| sum(!is.na(r$LOD)&r$LOD>=lod_thr(),na.rm=TRUE) | |
| }) | |
| df_bar <- data.frame(Model=names(counts),N_QTL=as.numeric(counts)) | |
| df_bar$Color <- MODEL_COLORS[df_bar$Model] | |
| df_bar$Color[is.na(df_bar$Color)] <- "#CC0000" | |
| plot_ly(df_bar, x=~Model, y=~N_QTL, type="bar", | |
| marker=list(color=~Color,line=list(color="#330000",width=1)), | |
| text=~N_QTL, textposition="outside", | |
| hovertext=~paste0(Model,": ",N_QTL," QTLs"), hoverinfo="text") %>% | |
| layout(title=list(text=paste0("QTLs per Model (LOD ≥ ",lod_thr(),")"), | |
| font=list(size=14)), | |
| xaxis=list(title="",color="#111111"), | |
| yaxis=list(title="# Significant QTLs",color="#111111"), | |
| plot_bgcolor="white",paper_bgcolor="white", | |
| font=list(color="#111111")) | |
| }) | |
| # ── COMMON QTL ── | |
| common_qtl_data <- reactive({ | |
| req(length(rv$compare_res)>0) | |
| find_common_qtl(rv$compare_res, lod_thr(), min_models=input$min_models) | |
| }) | |
| output$common_qtl_summary_ui <- renderUI({ | |
| cq <- common_qtl_data() | |
| if (is.null(cq)||nrow(cq)==0) | |
| return(div(class="info-box","⚠️ No common QTLs — run Compare Models first")) | |
| div(class="info-box", | |
| tags$b(nrow(cq)), " common QTLs detected across ≥ ", | |
| tags$b(input$min_models), " models") | |
| }) | |
| output$common_ideogram <- renderPlot({ | |
| cq <- common_qtl_data() | |
| req(!is.null(cq), nrow(cq)>0, rv$all_markers) | |
| cq_list <- list() | |
| for (mth in names(rv$compare_res)) { | |
| r <- rv$compare_res[[mth]] | |
| if (is.null(r)||"Message"%in%colnames(r)) next | |
| common_markers <- r[r$Marker %in% cq$Marker,] | |
| if (nrow(common_markers)>0) cq_list[[mth]] <- common_markers | |
| } | |
| par(bg="white") | |
| make_chromosome_ideogram_reference( | |
| cq_list, rv$all_markers, lod_thr(), | |
| title_str = "Common QTL (across ≥2 models)", | |
| band_dark_col = col_chr_dark(), | |
| band_light_col = col_chr_light(), | |
| border_col = col_chr_border(), | |
| qtl_size_min = input$qtl_marker_size_min, | |
| qtl_size_max = input$qtl_marker_size_max | |
| ) | |
| }, bg="white") | |
| output$common_bar <- renderPlotly({ | |
| cq <- common_qtl_data() | |
| req(!is.null(cq), nrow(cq)>0) | |
| df_bar <- cq[order(-cq$Max_LOD),][1:min(20,nrow(cq)),] | |
| plot_ly(df_bar, x=~Max_LOD, y=~reorder(Marker,Max_LOD), | |
| type="bar", orientation="h", | |
| marker=list(color="#CC0000",line=list(color="#660000",width=1)), | |
| text=~paste0(Marker," | ",N_Models_Detected," models"), | |
| hovertext=~paste0(Marker,"<br>Chr ",Chromosome," @ ",Position," cM<br>Max LOD: ",Max_LOD), | |
| hoverinfo="text") %>% | |
| layout(title=list(text="Top Common QTLs by Max LOD",font=list(size=14)), | |
| xaxis=list(title="Max LOD",color="#111111"), | |
| yaxis=list(title="",color="#111111"), | |
| plot_bgcolor="white",paper_bgcolor="white", | |
| font=list(color="#111111")) | |
| }) | |
| # ── TABLES ── | |
| output$qtl_table <- renderDT({ | |
| req(rv$scan_all) | |
| if ("Message"%in%colnames(rv$scan_all)) | |
| return(datatable(data.frame(Status=rv$scan_all$Message[1]))) | |
| sig <- rv$scan_all[!is.na(rv$scan_all$LOD)&rv$scan_all$LOD>=lod_thr(),,drop=FALSE] | |
| if (nrow(sig)==0) | |
| return(datatable(data.frame(Status="No significant QTLs at current threshold"))) | |
| sig <- add_lod_support_interval(sig,rv$scan_all,input$lod_drop) | |
| sig <- sig[order(-sig$LOD),] | |
| datatable(sig,options=list(scrollX=TRUE,pageLength=15),rownames=FALSE,class="compact stripe") | |
| }) | |
| output$compare_table <- renderDT({ | |
| req(length(rv$compare_res)>0) | |
| all_res <- lapply(names(rv$compare_res), function(mth){ | |
| r <- rv$compare_res[[mth]] | |
| if(is.null(r)||"Message"%in%colnames(r)) return(NULL) | |
| sig <- r[!is.na(r$LOD)&r$LOD>=lod_thr(),,drop=FALSE] | |
| if(nrow(sig)>0){sig$Model<-mth;sig} else NULL | |
| }) | |
| all_res <- do.call(rbind, Filter(Negate(is.null), all_res)) | |
| if (is.null(all_res)||nrow(all_res)==0) | |
| return(datatable(data.frame(Status="No significant QTLs"))) | |
| datatable(all_res[order(-all_res$LOD),],options=list(scrollX=TRUE,pageLength=15), | |
| rownames=FALSE,class="compact stripe") | |
| }) | |
| output$common_qtl_table <- renderDT({ | |
| cq <- common_qtl_data() | |
| if(is.null(cq)||nrow(cq)==0) | |
| return(datatable(data.frame(Status="Run Compare Models first"))) | |
| datatable(cq,options=list(scrollX=TRUE,pageLength=15),rownames=FALSE,class="compact stripe") | |
| }) | |
| # ============================================================ | |
| # DOWNLOADS — PNG with full custom settings | |
| # ============================================================ | |
| # Helper: save ideogram PNG with all custom colours/sizes | |
| save_ideogram_png <- function(qtl_list, markers, threshold, title_str, file, | |
| width, height) { | |
| png(file, width=width, height=height, res=150, bg="white") | |
| make_chromosome_ideogram_reference( | |
| qtl_list, markers, threshold, | |
| title_str = title_str, | |
| band_dark_col = col_chr_dark(), | |
| band_light_col = col_chr_light(), | |
| border_col = col_chr_border(), | |
| qtl_size_min = input$qtl_marker_size_min, | |
| qtl_size_max = input$qtl_marker_size_max | |
| ) | |
| dev.off() | |
| } | |
| # Helper: lty integer from style name (for base R PNG) | |
| lty_from_style <- function(style) { | |
| switch(style, "solid"=1, "dash"=2, "dot"=3, "dashdot"=4, 2) | |
| } | |
| # Manhattan PNG — base R, chromosome-per-panel, all custom settings | |
| output$dl_manhattan_png <- downloadHandler( | |
| filename = function() paste0("manhattan_chromwise_",input$mapping_method,"_",Sys.Date(),".png"), | |
| content = function(file) { | |
| req(rv$scan_all) | |
| pd <- rv$scan_all[!is.na(rv$scan_all$LOD) & !is.na(rv$scan_all$Position) & | |
| !is.na(rv$scan_all$Chromosome),] | |
| if (nrow(pd)==0) { png(file,100,100); dev.off(); return() } | |
| pd$Chromosome <- as.character(pd$Chromosome) | |
| pd$sig <- pd$LOD >= lod_thr() | |
| chroms <- sort(unique(pd$Chromosome)) | |
| n_chroms <- length(chroms) | |
| n_cols <- ceiling(sqrt(n_chroms)) | |
| n_rows <- ceiling(n_chroms / n_cols) | |
| png(file, width=input$manhattan_width, height=input$manhattan_height, res=150, bg="white") | |
| par(mfrow=c(n_rows, n_cols), mar=c(4,4,3,1), bg="white") | |
| for (chr in chroms) { | |
| chr_data <- pd[pd$Chromosome==chr,] | |
| chr_data <- chr_data[order(chr_data$Position),] | |
| y_max <- max(chr_data$LOD, na.rm=TRUE) * 1.1 | |
| plot(chr_data$Position, chr_data$LOD, type="n", | |
| xlab=paste("Chr",chr,"Position (cM)"), ylab="LOD Score", | |
| main=paste("Chr",chr), ylim=c(0,y_max), | |
| frame.plot=FALSE, col.main="#111111", cex.main=0.9, cex.lab=0.8) | |
| nonsig <- chr_data[!chr_data$sig,] | |
| sig_data <- chr_data[chr_data$sig,] | |
| if (nrow(nonsig)>0) | |
| points(nonsig$Position, nonsig$LOD, pch=16, | |
| cex=input$nonsig_point_size/5, col=col_nonsig()) | |
| # Significant — diamond, no text labels | |
| if (nrow(sig_data)>0) | |
| points(sig_data$Position, sig_data$LOD, pch=18, | |
| cex=input$sig_point_size/5, col=col_sig()) | |
| abline(h=lod_thr(), | |
| lty=lty_from_style(input$threshold_style), | |
| col=col_sig(), | |
| lwd=input$threshold_width) | |
| } | |
| dev.off() | |
| } | |
| ) | |
| # Ideogram PNG | |
| output$dl_idiogram_png <- downloadHandler( | |
| filename = function() paste0("idiogram_",input$mapping_method,"_",Sys.Date(),".png"), | |
| content = function(file) { | |
| req(rv$scan_all, rv$all_markers) | |
| res_list <- list() | |
| if (!is.null(rv$scan_all)&&!"Message"%in%colnames(rv$scan_all)) | |
| res_list[[input$mapping_method]] <- rv$scan_all | |
| save_ideogram_png(res_list, rv$all_markers, lod_thr(), | |
| paste(input$mapping_method,"— Trait:",input$pheno_col), | |
| file, input$ideogram_width, input$ideogram_height) | |
| } | |
| ) | |
| # Compare ideogram PNG | |
| output$dl_compare_idiogram_png <- downloadHandler( | |
| filename = function() paste0("compare_idiogram_",Sys.Date(),".png"), | |
| content = function(file) { | |
| req(length(rv$compare_res)>0, rv$all_markers) | |
| save_ideogram_png(rv$compare_res, rv$all_markers, lod_thr(), | |
| "Multi-Model QTL Comparison", | |
| file, input$ideogram_width, input$ideogram_height) | |
| } | |
| ) | |
| # Compare bar PNG — uses custom dimensions | |
| output$dl_compare_bar_png <- downloadHandler( | |
| filename = function() paste0("compare_bar_",Sys.Date(),".png"), | |
| content = function(file) { | |
| req(length(rv$compare_res)>0) | |
| counts <- sapply(rv$compare_res, function(r){ | |
| if(is.null(r)||"Message"%in%colnames(r)) return(0) | |
| sum(!is.na(r$LOD)&r$LOD>=lod_thr(),na.rm=TRUE) | |
| }) | |
| df_bar <- data.frame(Model=names(counts),N_QTL=as.numeric(counts)) | |
| df_bar$Color <- MODEL_COLORS[df_bar$Model] | |
| df_bar$Color[is.na(df_bar$Color)] <- "#CC0000" | |
| # Use manhattan width/height for bar chart download as well | |
| png(file, width=input$manhattan_width, height=max(400,input$manhattan_height/2), | |
| res=150, bg="white") | |
| par(mar=c(6,5,4,2), bg="white") | |
| bp <- barplot(df_bar$N_QTL, names.arg=df_bar$Model, | |
| col=df_bar$Color, border="#330000", | |
| main=paste0("QTLs per Model (LOD ≥ ",lod_thr(),")"), | |
| ylab="# Significant QTLs", las=2, cex.names=0.85, | |
| col.main="#111111") | |
| text(bp, df_bar$N_QTL + 0.1, df_bar$N_QTL, cex=0.8, font=2, col="#111111") | |
| dev.off() | |
| } | |
| ) | |
| # Common ideogram PNG | |
| output$dl_common_idiogram_png <- downloadHandler( | |
| filename = function() paste0("common_qtl_idiogram_",Sys.Date(),".png"), | |
| content = function(file) { | |
| cq <- common_qtl_data() | |
| req(!is.null(cq), nrow(cq)>0, rv$all_markers) | |
| cq_list <- list() | |
| for (mth in names(rv$compare_res)){ | |
| r <- rv$compare_res[[mth]] | |
| if(is.null(r)||"Message"%in%colnames(r)) next | |
| cm <- r[r$Marker %in% cq$Marker,] | |
| if(nrow(cm)>0) cq_list[[mth]] <- cm | |
| } | |
| save_ideogram_png(cq_list, rv$all_markers, lod_thr(), | |
| paste0("Common QTL (≥",input$min_models," models)"), | |
| file, input$ideogram_width, input$ideogram_height) | |
| } | |
| ) | |
| # ── CSV DOWNLOADS ── | |
| output$dl_qtl_csv <- downloadHandler( | |
| filename = function() paste0("qtl_significant_",input$mapping_method,"_",Sys.Date(),".csv"), | |
| content = function(file) { | |
| req(rv$scan_all) | |
| if("Message"%in%colnames(rv$scan_all)){write.csv(rv$scan_all,file,row.names=FALSE);return()} | |
| sig <- rv$scan_all[!is.na(rv$scan_all$LOD)&rv$scan_all$LOD>=lod_thr(),,drop=FALSE] | |
| if(nrow(sig)>0) sig <- add_lod_support_interval(sig,rv$scan_all,input$lod_drop) | |
| write.csv(sig[order(-sig$LOD),],file,row.names=FALSE) | |
| } | |
| ) | |
| output$dl_full_csv <- downloadHandler( | |
| filename = function() paste0("qtl_full_scan_",input$mapping_method,"_",Sys.Date(),".csv"), | |
| content = function(file) { | |
| req(rv$scan_all) | |
| write.csv(rv$scan_all,file,row.names=FALSE) | |
| } | |
| ) | |
| output$dl_compare_csv <- downloadHandler( | |
| filename = function() paste0("qtl_compare_",Sys.Date(),".csv"), | |
| content = function(file) { | |
| req(length(rv$compare_res)>0) | |
| all_res <- lapply(names(rv$compare_res), function(mth){ | |
| r <- rv$compare_res[[mth]] | |
| if(is.null(r)||"Message"%in%colnames(r)) return(NULL) | |
| sig <- r[!is.na(r$LOD)&r$LOD>=lod_thr(),,drop=FALSE] | |
| if(nrow(sig)>0){sig$Model<-mth;sig} else NULL | |
| }) | |
| all_res <- do.call(rbind, Filter(Negate(is.null),all_res)) | |
| if(is.null(all_res)) all_res <- data.frame(Status="No significant QTLs") | |
| write.csv(all_res,file,row.names=FALSE) | |
| } | |
| ) | |
| output$dl_common_csv <- downloadHandler( | |
| filename = function() paste0("qtl_common_",Sys.Date(),".csv"), | |
| content = function(file) { | |
| cq <- common_qtl_data() | |
| if(is.null(cq)||nrow(cq)==0) cq <- data.frame(Status="No common QTLs") | |
| write.csv(cq,file,row.names=FALSE) | |
| } | |
| ) | |
| } | |
| # ── Run ── | |
| shinyApp(ui=ui, server=server) |