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| library(BTR) | |
| library(ggplot2) | |
| library(gridExtra) #for plotting multiple graphs | |
| library(colorspace) | |
| library(reshape2) | |
| library(Hmisc) #for capitalisation. | |
| #(1) Load results and combine them. | |
| for(acyclic in c('acyclic', 'cyclic', 'both')) | |
| { | |
| for(single_cell in c(T, F)) | |
| { | |
| # sect_1 ------------------------------------------------------------------ | |
| path='~/btr_output' | |
| setwd(path) | |
| if(acyclic=='acyclic') | |
| { | |
| files = list.files(path=path, pattern=sprintf('^result.+_train_acyclic_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), full.names = T) | |
| } else if(acyclic=='cyclic') | |
| { | |
| files = list.files(path=path, pattern=sprintf('^result.+_train_cyclic_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), full.names = T) | |
| } else if(acyclic=='both') | |
| { | |
| files = list.files(path=path, pattern=sprintf('^result.+_train_[a-z]+_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), full.names = T) | |
| } | |
| # sect_2 ------------------------------------------------------------------ | |
| all_true_adj_mat = list() | |
| all_adj_mat = list() | |
| all_perf_score = c() | |
| all_time_taken = list() | |
| for(i in 1:length(files)) | |
| { | |
| if(acyclic=='acyclic') | |
| { | |
| tmp_name = gsub(sprintf('.+result_(.+)_train_acyclic_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), '\\1', files[i]) #extract name of method. | |
| } else if(acyclic=='cyclic') | |
| { | |
| tmp_name = gsub(sprintf('.+result_(.+)_train_cyclic_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), '\\1', files[i]) #extract name of method. | |
| } else if(acyclic=='both') | |
| { | |
| tmp_name = gsub(sprintf('.+result_(.+)_train_.+_%s_yeast[.]rda', ifelse(single_cell, 'sc', 'nonsc')), '\\1', files[i]) #extract name of method. | |
| } | |
| load(files[i]) #test_result. "true_adj_mat", "adj_mat", "perf_score", "time_taken" | |
| algo_true_adj_mat = list() | |
| algo_adj_mat = list() | |
| algo_perf_score = c() | |
| algo_time_taken = list() | |
| for(j in 1:length(test_result)) | |
| { | |
| algo_true_adj_mat = c(algo_true_adj_mat, list(test_result[[j]]$true_adj_mat)) | |
| algo_adj_mat = c(algo_adj_mat, list(test_result[[j]]$adj_mat)) | |
| algo_perf_score = c(algo_perf_score, list(test_result[[j]]$perf_score)) | |
| algo_time_taken = c(algo_time_taken, list(as.vector(test_result[[j]]$time_taken))) | |
| } | |
| algo_perf_score = do.call(rbind, algo_perf_score) | |
| algo_time_taken = do.call(rbind, algo_time_taken) | |
| if(tmp_name %in% names(all_true_adj_mat)) #only happen in acyclic=both. | |
| { | |
| all_true_adj_mat[[tmp_name]] = c(all_true_adj_mat[[tmp_name]], algo_true_adj_mat) | |
| all_adj_mat[[tmp_name]] = c(all_adj_mat[[tmp_name]], algo_adj_mat) | |
| all_perf_score[[tmp_name]] = rbind(all_perf_score[[tmp_name]], algo_perf_score) | |
| all_time_taken[[tmp_name]] = rbind(all_time_taken[[tmp_name]], algo_time_taken) | |
| } else | |
| { | |
| all_true_adj_mat = c(all_true_adj_mat, setNames(list(algo_true_adj_mat), tmp_name)) | |
| all_adj_mat = c(all_adj_mat, setNames(list(algo_adj_mat), tmp_name)) | |
| all_perf_score = c(all_perf_score, setNames(list(algo_perf_score), tmp_name)) | |
| all_time_taken = c(all_time_taken, setNames(list(algo_time_taken), tmp_name)) | |
| } | |
| } | |
| # sect_3 ------------------------------------------------------------------ | |
| #Prepare data for plotting. | |
| method_name = toupper(as.vector(sapply(names(all_perf_score), function(x) rep(x, nrow(all_perf_score[[1]]))))) | |
| network_name = rep(paste('net_', seq(1,nrow(all_perf_score[[1]])), sep=''), length(all_perf_score)) | |
| comb_df = do.call(rbind, all_perf_score) | |
| stopifnot(nrow(comb_df)==length(method_name)) | |
| comb_df = data.frame(method_name, network_name, comb_df, stringsAsFactors=F) | |
| comb_df[is.na(comb_df)] = 0 | |
| colnames(comb_df) = c(colnames(comb_df)[1:2], toupper(colnames(comb_df)[3:10])) | |
| colnames(comb_df)[10] = 'F-score' | |
| #Setting up consistent colour. | |
| plot_col = rainbow_hcl(length(unique(comb_df[,'method_name'])), alpha=0.8) | |
| names(plot_col) = unique(comb_df[,'method_name']) | |
| plot_df = melt(comb_df) | |
| #1st plot. | |
| comb_p1 = ggplot(plot_df, aes(x=plot_df[,'method_name'], y=plot_df[,"value"])) + | |
| geom_boxplot() + geom_jitter() + | |
| theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=20)) + | |
| xlab('Inference algorithms') + ylab('') + ggtitle(Hmisc:::capitalize(acyclic)) + | |
| facet_wrap(~variable, scales='free_y', ncol=2) | |
| png(paste('all_algorithms_', acyclic, ifelse(single_cell, '_sc', '_nonsc'), '_score.png', sep=''), width=3000, height=4000, res=300) | |
| plot(comb_p1) | |
| dev.off() | |
| #2nd plot | |
| tmp_list = list() | |
| for(i in unique(method_name)) | |
| { | |
| tmp_row = c(i, colMeans(subset(comb_df[,c(-1,-2)], comb_df[,'method_name']==i)), | |
| apply(subset(comb_df[,c(-1,-2)], comb_df[,'method_name']==i), 2, function(x) sd(x)/sqrt(length(x)))) | |
| #tmp_row = c(i, apply(subset(comb_df[,c(-1,-2)], comb_df[,'method_name']==i), 2, sum)) | |
| names(tmp_row) = c('method_name', 'p', 'np', 'r', 'a', 's', 'plr', 'nlr', 'f', 'p_se', 'np_se', 'r_se', 'a_se', 's_se', 'plr_se', 'nlr_se', 'f_se') | |
| tmp_list = c(tmp_list, list(tmp_row)) | |
| } | |
| combsum_df = do.call(rbind, tmp_list) | |
| tmp_rownames = combsum_df[,1] | |
| combsum_df = combsum_df[,-1] | |
| combsum_df = apply(combsum_df, 2, as.numeric) | |
| combsum_df = data.frame(combsum_df) | |
| combsum_df = cbind(method_name=tmp_rownames, combsum_df) | |
| plot_mid_df = melt(combsum_df) | |
| plot_val_df = subset(plot_mid_df, !grepl('[a-z]_se', plot_mid_df[,2])) | |
| plot_se_df = subset(plot_mid_df, grepl('[a-z]_se', plot_mid_df[,2])) | |
| plot_val_df = cbind(plot_val_df, plot_val_df[,3]-plot_se_df[,3], plot_val_df[,3]+plot_se_df[,3]) | |
| colnames(plot_val_df) = c('method_name', 'variable', 'value', 'low', 'high') | |
| combmid_p1 = ggplot(plot_val_df, aes(x=plot_val_df[,'method_name'], y=plot_val_df[,'value'], fill=method_name)) + | |
| geom_bar(stat="identity") + | |
| geom_errorbar(aes(ymin=plot_val_df[,"low"], ymax=plot_val_df[,"high"]), width=.5) + | |
| theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=20)) + guides(fill=FALSE) + | |
| scale_fill_manual("Legend", values = plot_col) + | |
| xlab('Inference algorithms') + ylab('') + ggtitle(Hmisc:::capitalize(acyclic)) + | |
| facet_wrap(~variable, scales='free_y', ncol=2) | |
| png(paste('all_algorithms_', acyclic, ifelse(single_cell, '_sc', '_nonsc'), '_meanscore.png', sep=''), width=3000, height=4000, res=300) | |
| plot(combmid_p1) | |
| dev.off() | |
| #Individual plots for F-scores | |
| comb_p8 = ggplot(comb_df, aes(x=reorder(comb_df[,'method_name'], -comb_df[,"F-score"], FUN=median), y=comb_df[,"F-score"])) + | |
| geom_boxplot() + geom_jitter() + | |
| theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=20)) + | |
| xlab('Inference algorithms') + ylab('F-score') + ggtitle(Hmisc:::capitalize(acyclic)) | |
| png(paste('all_algorithms_', acyclic, ifelse(single_cell, '_sc', '_nonsc'), '_fscore.png', sep=''), width=2000, height=2000, res=300) | |
| plot(comb_p8) | |
| dev.off() | |
| combmid_p8 = ggplot(combsum_df, aes(x=reorder(combsum_df[,'method_name'], -combsum_df[,"f"]), y=f, fill=method_name)) + | |
| geom_bar(stat="identity") + | |
| geom_errorbar(aes(ymin=combsum_df[,"f"] - combsum_df[,'f_se'], ymax=combsum_df[,"f"] + combsum_df[,'f_se']), width=.5) + | |
| theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=20)) + guides(fill=FALSE) + | |
| scale_fill_manual("Legend", values = plot_col) + | |
| xlab('Inference algorithms') + ylab('F-score') + ggtitle(Hmisc:::capitalize(acyclic)) | |
| png(paste('all_algorithms_', acyclic, ifelse(single_cell, '_sc', '_nonsc'), '_fmeanscore.png', sep=''), width=2000, height=2000, res=300) | |
| plot(combmid_p8) | |
| dev.off() | |
| } | |
| } | |