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annotate aldex2.R @ 1:75214276e2b7 draft default tip

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/aldex2 commit f9acc7c5710d6eb99a883cb83b7967e6af972f1b
author iuc
date Fri, 14 Jun 2024 21:21:29 +0000
parents f4d0bd4b4d6d
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f4d0bd4b4d6d planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/aldex2 commit b99f09cf03f075a6881d192b0f1233233289fa60
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1 #!/usr/bin/env Rscript
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3 suppressPackageStartupMessages(library("ALDEx2"))
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4 suppressPackageStartupMessages(library("data.table"))
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5 suppressPackageStartupMessages(library("qgraph"))
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6 suppressPackageStartupMessages(library("optparse"))
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7
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8 option_list <- list(
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9 make_option(c("--aldex_test"), action = "store", dest = "aldex_test", default = NULL, help = "Indicates which analysis to perform"),
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10 make_option(c("--analysis_type"), action = "store", dest = "analysis_type", help = "Indicates which analysis to perform"),
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11 make_option(c("--cutoff_effect"), action = "store", dest = "cutoff_effect", type = "integer", default = NULL, help = "Effect size cutoff for plotting"),
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12 make_option(c("--cutoff_pval"), action = "store", dest = "cutoff_pval", type = "double", default = NULL, help = "Benjamini-Hochberg fdr cutoff"),
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13 make_option(c("--denom"), action = "store", dest = "denom", help = "Indicates which features to retain as the denominator for the Geometric Mean calculation"),
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14 make_option(c("--effect"), action = "store", dest = "effect", default = "false", help = "Calculate abundances and effect sizes"),
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15 make_option(c("--feature_name"), action = "store", dest = "feature_name", default = NULL, help = "Name of the feature from the input data to be plotted"),
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16 make_option(c("--group_names"), action = "store", dest = "group_names", help = "Group names vector"),
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17 make_option(c("--group_nums"), action = "store", dest = "group_nums", default = NULL, help = "Group number for continuous numeric vector"),
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18 make_option(c("--hist_plot"), action = "store", dest = "hist_plot", default = "false", help = "Indicates whether to plot a histogram of p-values for the first Dirichlet Monte Carlo instance"),
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19 make_option(c("--include_sample_summary"), action = "store", dest = "include_sample_summary", default = "false", help = "Include median clr values for each sample"),
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20 make_option(c("--iterate"), action = "store", dest = "iterate", default = "false", help = "Indicates whether to iteratively perform a test"),
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21 make_option(c("--num_cols"), action = "store", dest = "num_cols", help = "Number of columns in group vector"),
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22 make_option(c("--num_cols_in_groups"), action = "store", dest = "num_cols_in_groups", default = NULL, help = "Number of columns in each group dewfining the continuous numeric vector"),
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23 make_option(c("--num_mc_samples"), action = "store", dest = "num_mc_samples", type = "integer", help = "Number of Monte Carlo samples"),
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24 make_option(c("--output"), action = "store", dest = "output", help = "output file"),
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25 make_option(c("--paired_test"), action = "store", dest = "paired_test", default = "false", help = "Indicates whether to do paired-sample tests"),
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26 make_option(c("--plot_test"), action = "store", dest = "plot_test", default = NULL, help = "The method of calculating significance"),
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27 make_option(c("--plot_type"), action = "store", dest = "plot_type", default = NULL, help = "The type of plot to be produced"),
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28 make_option(c("--reads"), action = "store", dest = "reads", help = "Input reads table"),
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29 make_option(c("--xlab"), action = "store", dest = "xlab", default = NULL, help = "x lable for the plot"),
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30 make_option(c("--ylab"), action = "store", dest = "ylab", default = NULL, help = "y lable for the plot")
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31 )
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33 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
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34 args <- parse_args(parser, positional_arguments = TRUE)
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35 opt <- args$options
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36
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37 get_boolean_value <- function(val) {
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38 if (val == "true") {
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39 return(TRUE)
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40 } else {
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41 return(FALSE)
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42 }
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43 }
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44
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45 # Read the input reads file into a data frame.
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46 reads_df <- read.table(file = opt$reads, header = TRUE, sep = "\t", row.names = 1, dec = ".", as.is = FALSE)
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47
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48 # Split the group_names and num_cols into lists of strings.
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49 group_names_str <- as.character(opt$group_names)
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50 group_names_list <- strsplit(group_names_str, ",")[[1]]
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51 num_cols_str <- as.character(opt$num_cols)
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52 num_cols_list <- strsplit(num_cols_str, ",")[[1]]
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53 # Construct conditions vector.
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54 conditions_vector <- c()
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55 for (i in seq_along(num_cols_list)) {
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56 num_cols <- as.integer(num_cols_list[i])
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57 group_name <- group_names_list[i]
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58 for (j in 1:num_cols) {
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59 conditions_vector <- cbind(conditions_vector, group_name)
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60 }
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61 }
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62 # The conditions_vector is now a matrix,
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63 # so coerce it back to a vector.
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64 conditions_vector <- as.vector(conditions_vector)
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65
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66 # Convert boolean values to boolean.
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67 effect <- get_boolean_value(opt$effect)
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68 include_sample_summary <- get_boolean_value(opt$include_sample_summary)
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69 iterate <- get_boolean_value(opt$iterate)
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70
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71 if (opt$analysis_type == "aldex") {
1
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72 aldex_obj <- aldex(
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73 reads = reads_df,
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74 conditions_vector,
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75 mc.samples = opt$num_mc_samples,
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76 test = opt$aldex_test,
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77 effect = effect,
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78 include.sample.summary = include_sample_summary,
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79 denom = opt$denom,
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80 iterate = iterate
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81 )
0
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82 } else {
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83 # Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each
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84 # instance using a log-ratio transform. This is the input for all further analyses.
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85 aldex_clr_obj <- aldex.clr(reads_df, conditions_vector, mc.samples = opt$num_mc_samples, denom = opt$denom)
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86
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87 if (opt$analysis_type == "aldex_corr") {
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88 if (!is.null(opt$cont_var)) {
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89 # Read the input cont_var vector.
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90 cont_var <- as.numeric(read.table(file = opt$cont_var, header = FALSE, sep = "\t"))
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91 }
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92
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93 # Split the group_names and num_cols into lists of strings.
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94 group_nums_str <- as.character(opt$group_nums)
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95 group_nums_list <- strsplit(group_nums_str, ",")[[1]]
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96 num_cols_in_groups_str <- as.character(opt$num_cols_in_groups)
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97 num_cols_in_groups_list <- strsplit(num_cols_in_groups_str, ",")[[1]]
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98 # Construct continuous numeric vector.
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99 cont_var_vector <- c()
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100 for (i in seq_along(num_cols_in_groups_list)) {
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101 num_cols_in_group <- as.integer(num_cols_in_groups_list[i])
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102 group_num <- group_nums_list[i]
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103 for (j in 1:num_cols_in_group) {
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104 cont_var_vector <- cbind(cont_var_vector, group_num)
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105 }
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106 }
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107 # The cont_var_vector is now a matrix,
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108 # so coerce it back to a vector.
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109 cont_var_vector <- as.numeric(as.vector(cont_var_vector))
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110
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111 aldex_obj <- aldex.corr(aldex_clr_obj, cont.var = cont_var_vector)
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112 } else if (opt$analysis_type == "aldex_effect") {
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113 aldex_obj <- aldex.effect(aldex_clr_obj, include_sample_summary)
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114 } else if (opt$analysis_type == "aldex_expected_distance") {
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115 dist <- aldex.expectedDistance(aldex_clr_obj)
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116 png(filename = opt$output)
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117 qgraph(dist, layout = "spring", vsize = 1)
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118 dev.off()
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119 } else if (opt$analysis_type == "aldex_kw") {
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120 aldex_obj <- aldex.kw(aldex_clr_obj)
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121 } else if (opt$analysis_type == "aldex_plot") {
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122 aldex_obj <- aldex(
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123 reads = reads_df,
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124 conditions_vector,
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125 mc.samples = opt$num_mc_samples,
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126 test = opt$aldex_test,
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127 effect = effect,
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128 include.sample.summary = include_sample_summary,
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129 denom = opt$denom,
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130 iterate = iterate
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131 )
0
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132 png(filename = opt$output)
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133 aldex.plot(
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134 x = aldex_obj,
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135 type = opt$plot_type,
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136 test = opt$plot_test,
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137 cutoff.pval = opt$cutoff_pval,
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138 cutoff.effect = opt$cutoff_effect,
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139 xlab = opt$xlab,
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140 ylab = opt$ylab
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141 )
0
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142 dev.off()
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143 } else if (opt$analysis_type == "aldex_plot_feature") {
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144 png(filename = opt$output)
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145 aldex.plotFeature(aldex_clr_obj, opt$feature_name)
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146 dev.off()
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147 } else if (opt$analysis_type == "aldex_ttest") {
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148 paired_test <- get_boolean_value(opt$paired_test)
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149 hist_plot <- get_boolean_value(opt$hist_plot)
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150 aldex_obj <- aldex.ttest(aldex_clr_obj, paired.test = paired_test, hist.plot = hist_plot)
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151 }
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152 }
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153 if ((opt$analysis_type != "aldex_expected_distance") && (opt$analysis_type != "aldex_plot") && (opt$analysis_type != "aldex_plot_feature")) {
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154 # Output the ALDEx object.
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155 write.table(aldex_obj, file = opt$output, append = FALSE, sep = "\t", dec = ".", row.names = FALSE, col.names = TRUE)
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156 }