Mercurial > repos > ufz > dose_response_analysis_tool

annotate dose_response.R @ 2:c122403ac78a draft

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planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 61a3d9a20a9a90d551dd5f7503be781dc28f4b75
author ufz
date Wed, 18 Dec 2024 09:11:40 +0000
parents 8a1b524ed9d8
children 2aa9da0a84a4
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082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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1 library(drc)
082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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2 library(ggplot2)
082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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4 fit_models <- function(data, concentration_col, response_col) {
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5 models <- list(
082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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6 LL.2 = drm(data[[response_col]] ~ data[[concentration_col]], data = data, fct = LL.2(), type = "binomial"),
082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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7 LL.4 = drm(data[[response_col]] ~ data[[concentration_col]], data = data, fct = LL.4(), type = "binomial"),
082e9d22c38d planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 3aebdcc7c5b266a30262402934ffaad2a58adbcb
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8 W1.4 = drm(data[[response_col]] ~ data[[concentration_col]], data = data, fct = W1.4(), type = "binomial"),
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9 W2.4 = drm(data[[response_col]] ~ data[[concentration_col]], data = data, fct = W2.4(), type = "binomial"),
c122403ac78a planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 61a3d9a20a9a90d551dd5f7503be781dc28f4b75
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10 BC.5 = drm(data[[response_col]] ~ data[[concentration_col]], data = data, fct = BC.5(), type = "binomial")
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11 )
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12 return(models)
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13 }
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15 select_best_model <- function(models) {
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16 aic_values <- sapply(models, AIC)
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17 best_model_name <- names(which.min(aic_values))
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18 best_model <- models[[best_model_name]]
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19 return(list(name = best_model_name, model = best_model))
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20 }
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22 calculate_ec_values <- function(model) {
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23 ec50 <- ED(model, 50, type = "relative")
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24 ec25 <- ED(model, 25, type = "relative")
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25 ec10 <- ED(model, 10, type = "relative")
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26 return(list(EC50 = ec50, EC25 = ec25, EC10 = ec10))
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27 }
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29 plot_dose_response <- function(model, data, ec_values, concentration_col, response_col, plot_file, compound_name, concentration_unit) {
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30 # Generate a grid of concentration values for predictions
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31 concentration_grid <- seq(min(data[[concentration_col]]), max(data[[concentration_col]]), length.out = 100)
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32 prediction_data <- data.frame(concentration_grid)
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33 colnames(prediction_data) <- concentration_col
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35 # Compute predictions with confidence intervals
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36 predictions <- predict(model, newdata = prediction_data, type = "response", interval = "confidence")
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37 prediction_data$resp <- predictions[, 1]
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38 prediction_data$lower <- predictions[, 2]
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39 prediction_data$upper <- predictions[, 3]
c122403ac78a planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tools/tox_tools/baseline_calculator commit 61a3d9a20a9a90d551dd5f7503be781dc28f4b75
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41 print(prediction_data)
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43 data$rep <- factor(data$rep)
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45 # Create the plot
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46 p <- ggplot(data, aes_string(x = concentration_col, y = response_col)) +
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47 geom_point(aes(colour = rep)) + # Original data points
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48 geom_line(data = prediction_data, aes_string(x = "conc", y = "resp"), color = "blue") + # Predicted curve
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49 geom_ribbon(data = prediction_data, aes_string(x = "conc", ymin = "lower", ymax = "upper"), alpha = 0.2, fill = "blue") + # Confidence intervals
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50 geom_vline(xintercept = ec_values$EC10[1], color = "green", linetype = "dashed") +
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51 geom_vline(xintercept = ec_values$EC50[1], color = "red", linetype = "dashed") +
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52 labs(
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53 title = paste(compound_name, "- Dose-Response Curve"),
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54 x = paste("Dose [", concentration_unit, "]"),
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55 y = "Response %"
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56 ) +
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57 theme_minimal() +
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58 theme(
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59 panel.background = element_rect(fill = "white", color = NA),
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60 plot.background = element_rect(fill = "white", color = NA)
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61 )
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62
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63 # Save the plot to a file
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64 jpeg(filename = plot_file, width = 480, height = 480, res = 72)
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65 print(p)
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66 dev.off()
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67 }
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68
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69 dose_response_analysis <- function(data, concentration_col, response_col, plot_file, ec_file, compound_name, concentration_unit) {
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70 # Ensure column names are correctly selected
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71 concentration_col <- colnames(data)[as.integer(concentration_col)]
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72 response_col <- colnames(data)[as.integer(response_col)]
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74 # Fit models and select the best one
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75 models <- fit_models(data, concentration_col, response_col)
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76 best_model_info <- select_best_model(models)
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77 best_model <- best_model_info$model
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78 best_model_name <- best_model_info$name
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79
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80 # Calculate EC values
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81 ec_values <- calculate_ec_values(best_model)
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82
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83 # Plot the dose-response curve
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84 plot_dose_response(best_model, data, ec_values, concentration_col, response_col, plot_file, compound_name, concentration_unit)
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86 # Get model summary and AIC value
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87 model_summary <- summary(best_model)
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88 model_aic <- AIC(best_model)
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89
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90 # Prepare EC values data frame with additional information
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91 ec_data <- data.frame(
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92 Metric = c("chemical_name", "EC10", "EC25", "EC50", "AIC"),
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93 Value = c(compound_name, ec_values$EC10[1], ec_values$EC25[1], ec_values$EC50[1], model_aic)
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94 )
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96 # Write EC values, AIC, and model summary to the output file
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97 write.table(ec_data, ec_file, sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
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98
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99 # Append the model summary to the file
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100 cat("\nModel Summary:\n", file = ec_file, append = TRUE)
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101 capture.output(model_summary, file = ec_file, append = TRUE)
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102
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103 return(list(best_model = best_model_name, ec_values = ec_values))
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104 }
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105
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106 args <- commandArgs(trailingOnly = TRUE)
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107
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108 data_file <- args[1]
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109 concentration_col <- args[2]
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110 response_col <- args[3]
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111 plot_file <- args[4]
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112 ec_file <- args[5]
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113 compound_name <- args[6]
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114 concentration_unit <- args[7]
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115
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116 data <- read.csv(data_file, header = TRUE, sep = "\t")
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117 print(data)
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118 dose_response_analysis(data, concentration_col, response_col, plot_file, ec_file, compound_name, concentration_unit)