Mercurial > repos > artbio > gsc_mannwhitney_de

annotate MannWhitney_DE.R @ 5:a7eb04240b8b draft default tip

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_mannwhitney_de commit 9286176209be97cd4b972c034345cf060bd7783e
author artbio
date Thu, 07 Nov 2024 21:37:11 +0000
parents 6916ac5a9ef0
children
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6916ac5a9ef0 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_mannwhitney_de commit c394391dcf541d91ee1dfdc0c3d80cd7a21942ff
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1 # Perform a differential analysis between 2 groups of cells.
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3 # Example of command
c67dba545a37 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_mannwhitney_de commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
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4 # Rscript MannWhitney_DE.R --input <input.tsv> --sep <tab> --colnames <TRUE> --metadata <signature.tsv> --column_name <rate> --fdr <0.01> --output <diff_analysis.tsv>
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5
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6 options(
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7 show.error.messages = FALSE,
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8 error = function() {
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9 cat(geterrmessage(), file = stderr())
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10 q("no", 1, FALSE)
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11 }
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12 )
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13
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14 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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16 suppressPackageStartupMessages({
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17 library(optparse)
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18 })
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20 sessionInfo()
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22 option_list <- list(
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23 make_option(
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24 "--input",
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25 default = NA,
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26 type = "character",
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27 help = "Input file that contains log2(CPM +1) values"
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28 ),
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29 make_option(
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30 "--sep",
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31 default = "\t",
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32 type = "character",
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33 help = "File separator [default : '%default' ]"
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34 ),
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35 make_option(
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36 "--colnames",
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37 default = TRUE,
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38 type = "logical",
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39 help = "Consider first line as header ? [default : '%default' ]"
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40 ),
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41 make_option(
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42 "--comparison_factor_file",
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43 default = NA,
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44 type = "character",
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45 help = " A two column table : cell identifiers and a comparison factor that split cells in two categories (high/low, HOM/HET,...)"
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46 ),
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47 make_option(
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48 "--factor1",
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49 type = "character",
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50 help = "level associated to the control condition in the factor file"
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51 ),
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52 make_option(
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53 "--factor2",
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54 type = "character",
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55 help = "level associated to the test condition in the factor file"
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56 ),
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57 make_option(
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58 "--fdr",
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59 default = 0.01,
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60 type = "numeric",
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61 help = "FDR threshold [default : '%default' ]"
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62 ),
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63 make_option(
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64 "--log",
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65 default = FALSE,
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66 action = "store_true",
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67 type = "logical",
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68 help = "Expression data are log-transformed [default : '%default' ]"
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69 ),
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70 make_option(
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71 "--output",
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72 default = "results.tsv",
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73 type = "character",
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74 help = "Output name [default : '%default' ]"
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75 )
0
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76 )
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77
4
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78 opt <- parse_args(OptionParser(option_list = option_list),
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79 args = commandArgs(trailingOnly = TRUE)
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80 )
0
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81
4
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82 if (opt$sep == "tab") {
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83 opt$sep <- "\t"
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84 }
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85 if (opt$sep == "comma") {
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86 opt$sep <- ","
4
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87 }
0
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88
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89 # Open files
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90 data.counts <- read.table(
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91 opt$input,
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92 h = opt$colnames,
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93 row.names = 1,
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94 sep = opt$sep,
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95 check.names = FALSE
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96 )
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98 metadata <- read.table(
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99 opt$comparison_factor_file,
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100 header = TRUE,
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101 stringsAsFactors = FALSE,
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102 sep = "\t",
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103 check.names = FALSE,
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104 row.names = 1
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105 )
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106
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107 metadata <- subset(metadata, rownames(metadata) %in% colnames(data.counts))
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108
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109 # Create two logical named vectors for each factor level of cell signature
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110 factor1_cells <- setNames(metadata[, 1] == opt$factor1, rownames(metadata))
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111 factor2_cells <- setNames(metadata[, 1] == opt$factor2, rownames(metadata))
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112
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113 ## Mann-Whitney test (Two-sample Wilcoxon test)
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114 MW_test <- data.frame(t(apply(data.counts, 1, function(x) {
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115 do.call("cbind", wilcox.test(x[names(factor1_cells)[factor1_cells]], x[names(factor2_cells)[factor2_cells]]))[, 1:2]
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116 })), stringsAsFactors = FALSE)
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117
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118 # Benjamini-Hochberg correction and significativity
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119 MW_test$p.adjust <- p.adjust(as.numeric(MW_test$p.value), method = "BH", n = nrow(MW_test))
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120 MW_test$Significant <- MW_test$p.adjust < opt$fdr
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121
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122 ## Descriptive Statistics Function
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123 descriptive_stats <- function(InputData) {
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124 SummaryData <- data.frame(
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125 mean = rowMeans(InputData),
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126 SD = apply(InputData, 1, sd),
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127 Variance = apply(InputData, 1, var),
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128 Percentage_Detection = apply(InputData, 1, function(x, y = InputData) {
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129 (sum(x != 0) / ncol(y)) * 100
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130 }),
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131 mean_condition2 = rowMeans(InputData[, factor2_cells]),
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132 mean_condition1 = rowMeans(InputData[, factor1_cells])
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133 )
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134 if (opt$log) {
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135 SummaryData$log2FC <- SummaryData$mean_condition2 - SummaryData$mean_condition1
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136 } else {
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137 SummaryData$log2FC <- log2(SummaryData$mean_condition2 / SummaryData$mean_condition1)
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138 }
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139 return(SummaryData)
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140 }
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141
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142 gene_stats <- descriptive_stats(data.counts)
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143
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144 results <- merge(gene_stats, MW_test, by = "row.names")
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145 colnames(results)[1] <- "genes"
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146
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147 ## Annotate Significant column
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148 results$Significant[results$Significant == TRUE & !is.na(results$Significant)] <- ifelse(subset(results, Significant == TRUE)$log2FC > 0, "UP", "DOWN")
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149 results$Significant[results$Significant == FALSE & !is.na(results$Significant)] <- "NS"
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151
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152 # Save files
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153 write.table(
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154 results[order(results$p.adjust), ],
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155 opt$output,
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156 sep = "\t",
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157 quote = FALSE,
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158 col.names = TRUE,
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159 row.names = FALSE
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160 )