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annotate scripts/seurat-scale-data.R @ 6:07fc9f4841b3 draft default tip

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planemo upload commit 0264c359f1d638bbbbab515a3502231f679cdcf6
author ebi-gxa
date Sat, 02 Mar 2024 10:42:01 +0000
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6
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1 #!/usr/bin/env Rscript
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2
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3 # Load optparse we need to check inputs
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4
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5 suppressPackageStartupMessages(require(optparse))
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6
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7 # Load common functions
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8
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9 suppressPackageStartupMessages(require(workflowscriptscommon))
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10
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11 # parse options
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12
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13 option_list = list(
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14 make_option(
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15 c("-i", "--input-object-file"),
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16 action = "store",
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17 default = NA,
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18 type = 'character',
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19 help = "File name in which a serialized R matrix object may be found."
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20 ),
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21 make_option(
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22 c("--input-format"),
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23 action = "store",
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24 default = "seurat",
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25 type = 'character',
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26 help = "Either loom, seurat, anndata or singlecellexperiment for the input format to read."
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27 ),
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28 make_option(
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29 c("--output-format"),
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30 action = "store",
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31 default = "seurat",
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32 type = 'character',
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33 help = "Either loom, seurat, anndata or singlecellexperiment for the output format."
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34 ),
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35 make_option(
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36 c("-e", "--genes-use"),
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37 action = "store",
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38 default = NULL,
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39 type = 'character',
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40 help = "File with gene names to scale/center (one gene per line). Default is all genes in object@data."
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41 ),
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42 make_option(
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43 c("-v", "--vars-to-regress"),
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44 action = "store",
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45 default = NULL,
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46 type = 'character',
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47 help = "Comma-separated list of variables to regress out (previously latent.vars in RegressOut). For example, nUMI, or percent.mito."
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48 ),
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49 make_option(
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50 c("-m", "--model-use"),
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51 action = "store",
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52 default = 'linear',
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53 type = 'character',
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54 help = "Use a linear model or generalized linear model (poisson, negative binomial) for the regression. Options are 'linear' (default), 'poisson', and 'negbinom'."
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55 ),
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56 make_option(
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57 c("-u", "--use-umi"),
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58 action = "store",
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59 default = FALSE,
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60 type = 'logical',
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61 help = "Regress on UMI count data. Default is FALSE for linear modeling, but automatically set to TRUE if model.use is 'negbinom' or 'poisson'."
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62 ),
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63 make_option(
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64 c("-s", "--do-not-scale"),
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65 action = "store_true",
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66 default = FALSE,
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67 type = 'logical',
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68 help = "Skip the data scale."
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69 ),
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70 make_option(
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71 c("-c", "--do-not-center"),
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72 action = "store_true",
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73 default = FALSE,
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74 type = 'logical',
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75 help = "Skip data centering."
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76 ),
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77 make_option(
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78 c("-x", "--scale-max"),
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79 action = "store",
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80 default = 10,
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81 type = 'double',
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82 help = "Max value to return for scaled data. The default is 10. Setting this can help reduce the effects of genes that are only expressed in a very small number of cells. If regressing out latent variables and using a non-linear model, the default is 50."
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83 ),
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84 make_option(
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85 c("-b", "--block-size"),
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86 action = "store",
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87 default = 1000,
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88 type = 'integer',
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89 help = "Default size for number of genes to scale at in a single computation. Increasing block.size may speed up calculations but at an additional memory cost."
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90 ),
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91 make_option(
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92 c("-d", "--min-cells-to-block"),
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93 action = "store",
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94 default = 1000,
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95 type = 'integer',
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96 help = "If object contains fewer than this number of cells, don't block for scaling calculations."
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97 ),
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98 make_option(
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99 c("-n", "--check-for-norm"),
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100 action = "store",
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101 default = TRUE,
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102 type = 'logical',
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103 help = "Check to see if data has been normalized, if not, output a warning (TRUE by default)."
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104 ),
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105 make_option(
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106 c("-o", "--output-object-file"),
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107 action = "store",
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108 default = NA,
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109 type = 'character',
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110 help = "File name in which to store serialized R object of type 'Seurat'.'"
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111 )
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112 )
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113
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114 opt <- wsc_parse_args(option_list, mandatory = c('input_object_file', 'output_object_file'))
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115
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116 # Check parameter values
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117
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118 if ( ! file.exists(opt$input_object_file)){
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119 stop((paste('File', opt$input_object_file, 'does not exist')))
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120 }
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121
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122 if (! is.null(opt$genes_use)){
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123 if (! file.exists(opt$genes_use)){
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124 stop((paste('Supplied genes file', opt$genes_use, 'does not exist')))
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125 }else{
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126 genes_use <- readLines(opt$genes_use)
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127 }
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128 }else{
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129 genes_use <- NULL
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130 }
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131
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132 # break up opt$vars_to_regress into a list if it has commas
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133 opt$vars_to_regress <- unlist(strsplit(opt$vars_to_regress, ","))
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134
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135 # Now we're hapy with the arguments, load Seurat and do the work
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136
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137 suppressPackageStartupMessages(require(Seurat))
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138 if(opt$input_format == "loom" | opt$output_format == "loom") {
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139 suppressPackageStartupMessages(require(SeuratDisk))
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140 } else if(opt$input_format == "singlecellexperiment" | opt$output_format == "singlecellexperiment") {
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141 suppressPackageStartupMessages(require(scater))
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142 }
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143
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144 # Input from serialized R object
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145
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146 seurat_object <- read_seurat4_object(input_path = opt$input_object_file, format = opt$input_format)
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147 # https://stackoverflow.com/questions/9129673/passing-list-of-named-parameters-to-function
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148 # might be useful
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149 scaled_seurat_object <- ScaleData(seurat_object,
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150 features = genes_use,
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151 vars.to.regress = opt$vars_to_regress,
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152 model.use = opt$model_use,
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153 use.umi = opt$use_umi,
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154 do.scale = !opt$do_not_scale,
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155 do.center = !opt$do_not_center,
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156 scale.max = opt$scale_max,
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157 block.size = opt$block_size,
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158 min.cells.to.block = opt$min_cells_to_block,
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159 verbose = FALSE)
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160
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161
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162 # Output to a serialized R object
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163 write_seurat4_object(seurat_object = scaled_seurat_object,
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164 output_path = opt$output_object_file,
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165 format = opt$output_format)