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annotate signature_score.R @ 0:bf99be04d098 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_signature_score commit 70c7abb20f76651e61325e3aa90809f6874f8b85
author artbio
date Mon, 24 Jun 2019 07:22:06 -0400
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bf99be04d098 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_signature_score commit 70c7abb20f76651e61325e3aa90809f6874f8b85
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1 #########################
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2 # Signature score #
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3 #########################
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4
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5 # Compute the signature score based on the geometric mean of the target gene expression
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6 # and split cells in 2 groups (high/low) using this signature score.
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7
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8 # Example of command
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9 # Rscript 4-signature_score.R --input <input.tsv> --genes ARNT2,SALL2,SOX9,OLIG2,POU3F2
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10 # --output <output.tab> --pdf <output.pdf>
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11
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12 # load packages that are provided in the conda env
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13 options( show.error.messages=F,
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14 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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15 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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16 warnings()
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17
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18 library(optparse)
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19 library(psych)
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20 library(ggplot2)
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21 library(gridExtra)
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22
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23 # Arguments
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24 option_list = list(
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25 make_option(
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26 "--input",
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27 default = NA,
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28 type = 'character',
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29 help = "Input file that contains log2(CPM +1) values"
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30 ),
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31 make_option(
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32 "--sep",
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33 default = '\t',
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34 type = 'character',
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35 help = "File separator [default : '%default' ]"
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36 ),
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37 make_option(
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38 "--colnames",
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39 default = TRUE,
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40 type = 'logical',
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41 help = "Consider first line as header ? [default : '%default' ]"
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42 ),
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43 make_option(
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44 "--genes",
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45 default = NA,
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46 type = 'character',
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47 help = "List of genes comma separated"
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48 ),
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49 make_option(
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50 "--percentile_threshold",
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51 default = 20,
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52 type = 'integer',
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53 help = "detection threshold to keep a gene in signature set [default : '%default' ]"
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54 ),
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55 make_option(
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56 "--output",
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57 default = "./output.tab",
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58 type = 'character',
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59 help = "Output path [default : '%default' ]"
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60 ),
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61 make_option(
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62 "--stats",
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63 default = "./statistics.tab",
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64 type = 'character',
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65 help = "statistics path [default : '%default' ]"
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66 ),
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67 make_option(
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68 "--pdf",
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69 default = "~/output.pdf",
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70 type = 'character',
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71 help = "pdf path [default : '%default' ]"
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72 )
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73 )
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74
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75 opt = parse_args(OptionParser(option_list = option_list),
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76 args = commandArgs(trailingOnly = TRUE))
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77
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78 if (opt$sep == "tab") {opt$sep = "\t"}
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79 if (opt$sep == "comma") {opt$sep = ","}
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80
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81 # Take input data
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82 data.counts <- read.table(
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83 opt$input,
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84 h = opt$colnames,
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85 row.names = 1,
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86 sep = opt$sep,
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87 check.names = F
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88 )
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89
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90 # Get vector of target genes
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91 genes <- unlist(strsplit(opt$genes, ","))
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92
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93 if (length(unique(genes %in% rownames(data.counts))) == 1) {
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94 if (unique(genes %in% rownames(data.counts)) == F)
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95 stop("None of these genes are in your dataset: ", opt$genes)
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96 }
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97
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98 logical_genes <- rownames(data.counts) %in% genes
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99
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100 # Retrieve target genes in counts data
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101 signature.counts <- subset(data.counts, logical_genes)
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102
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103
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104 ## Descriptive Statistics Function
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105 descriptive_stats = function(InputData) {
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106 SummaryData = data.frame(
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107 mean = rowMeans(InputData),
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108 SD = apply(InputData, 1, sd),
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109 Variance = apply(InputData, 1, var),
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110 Percentage_Detection = apply(InputData, 1, function(x, y = InputData) {
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111 (sum(x != 0) / ncol(y)) * 100
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112 })
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113 )
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114 return(SummaryData)
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115 }
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116
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117 signature_stats <- descriptive_stats(signature.counts)
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118
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119 # Find poorly detected genes from the signature
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120 kept_genes <- signature_stats$Percentage_Detection >= opt$percentile_threshold
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121
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122 # Add warnings
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123 if (length(unique(kept_genes)) > 1) {
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124 cat(
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125 "WARNINGS ! Following genes were removed from further analysis due to low gene expression :",
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126 paste(paste(rownames(signature.counts)[!kept_genes], round(signature_stats$Percentage_Detection[!kept_genes], 2), sep = " : "), collapse = ", "),
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127 "\n"
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128 )
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129 } else {
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130 if (unique(kept_genes) == F) {
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131 stop(
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132 "None of these genes are detected in ",
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133 opt$percent,
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134 "% of your cells: ",
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135 paste(rownames(signature_stats), collapse = ", "),
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136 ". You can be less stringent thanks to --percent parameter."
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137 )
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138 }
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139 }
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140
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141 # Remove genes poorly detected in the dataset
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142 signature.counts <- signature.counts[kept_genes,]
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143
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144 # Replace 0 by 1 counts
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145 signature.counts[signature.counts == 0] <- 1
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146
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147 # Geometric mean by cell
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148 score <- apply(signature.counts, 2, geometric.mean) # geometric.mean requires psych
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149
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150 # Add results in signature_output
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151 signature_output <- data.frame(
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152 cell = names(score),
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153 score = score,
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154 rate = ifelse(score > mean(score), "HIGH", "LOW"),
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155 nGenes = colSums(data.counts != 0),
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156 total_counts = colSums(data.counts)
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157 )
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158
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159 # statistics of input genes, signature genes first lines
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160 statistics.counts <- rbind(subset(data.counts, logical_genes),
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161 subset(data.counts, !logical_genes))
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162 statistics <- descriptive_stats(statistics.counts)
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163 statistics <- cbind(gene=rownames(statistics), statistics)
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164
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165
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166
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167 # Re-arrange score matrix for plots
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168 score <- data.frame(score = score,
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169 order = rank(score, ties.method = "first"),
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170 signature = signature_output$rate,
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171 stringsAsFactors = F)
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172
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173 pdf(file = opt$pdf)
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174
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175 ggplot(score, aes(x = order, y = score)) +
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176 geom_line() +
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177 geom_segment(x = 0, xend = max(score$order[score$signature == "LOW"]), y = mean(score$score), yend = mean(score$score)) +
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178 geom_area(aes(fill = signature), alpha = .7) +
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179 scale_fill_manual(values=c("#ff0000", "#08661e")) +
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180 geom_text(aes(x = 1, y = mean(score)), label = "Mean", vjust = -0.3, colour = "black") +
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181 labs(title = "Ordered cell signature scores", x = "Cell index", y = "Score")
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182
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183 density_score <- density(score$score)
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184 ggplot(data.frame(density_score[1:2]), aes(x, y, fill = ifelse(x < mean(score$score), "LOW", "HIGH"))) +
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185 geom_line() +
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186 geom_vline(xintercept = mean(score$score)) +
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187 geom_text(x = mean(score$score), y = max(density_score$y), label = "Mean", hjust = -0.3, colour = "black") +
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188 geom_area(alpha = .7) +
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189 scale_fill_manual(values=c("#ff0000", "#08661e")) +
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190 ylim(0, max(density_score$y)) +
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191 labs(
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192 title = "Distribution of Cell signature scores",
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193 x = paste("N =", density_score$n, "Bandwidth =", density_score$bw),
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194 y = "Density",
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195 fill = "Signature"
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196 )
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197
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198 # Check score independant of low expression
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199 p_gene <- ggplot(signature_output, aes(rate, nGenes)) +
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200 geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) +
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201 scale_fill_manual(values=c("#ff0000", "#08661e")) +
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202 geom_jitter() + labs(y = "Number of detected genes", x = "Signature")
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203
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204 p_counts <- ggplot(signature_output, aes(rate, total_counts)) +
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205 geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) +
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206 scale_fill_manual(values=c("#ff0000", "#08661e")) +
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207 geom_jitter() + labs(y = "Total counts", x = "Signature")
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208
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209 grid.arrange(p_gene, p_counts, ncol = 2, top = "Influence of library sequencing depth on cell signature scores")
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210
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211 dev.off()
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212
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213 # Save file
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214 write.table(
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215 signature_output,
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216 opt$output,
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217 sep = "\t",
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218 quote = F,
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219 col.names = T,
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220 row.names = F
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221 )
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222
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223 write.table(
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224 statistics,
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225 opt$stats,
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226 sep = "\t",
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227 quote = F,
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228 col.names = T,
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229 row.names = F
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230 )