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annotate scater-manual-filter.R @ 0:e6ca62ac65c6 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
author iuc
date Thu, 18 Jul 2019 11:13:41 -0400
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e6ca62ac65c6 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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1 #!/usr/bin/env Rscript
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2 # Manually filter SingleCellExperiment with user-defined parameters
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3
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4 # Load optparse we need to check inputs
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5 library(optparse)
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6 library(workflowscriptscommon)
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7 library(LoomExperiment)
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8 library(scater)
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9
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10 # parse options
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11 option_list = list(
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12 make_option(
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13 c("-i", "--input-loom"),
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14 action = "store",
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15 default = NA,
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16 type = 'character',
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17 help = "A SingleCellExperiment object file in Loom format."
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18 ),
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19 make_option(
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20 c("-d", "--detection-limit"),
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21 action = "store",
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22 default = 0,
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23 type = 'numeric',
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24 help = "Numeric scalar providing the value above which observations are deemed to be expressed"
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25 ),
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26 make_option(
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27 c("-l", "--library-size"),
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28 action = "store",
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29 default = 0,
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30 type = 'numeric',
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31 help = "Minimum library size (mapped reads) to filter cells on"
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32 ),
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33 make_option(
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34 c("-m", "--percent-counts-MT"),
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35 action = "store",
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36 default = 100,
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37 type = 'numeric',
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38 help = "Maximum % of mitochondrial genes expressed per cell. Cells that exceed this value will be filtered out."
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39 ),
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40 make_option(
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41 c("-o", "--output-loom"),
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42 action = "store",
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43 default = NA,
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44 type = 'character',
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45 help = "File name in which to store the SingleCellExperiment object in Loom format."
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46 )
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47 )
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48
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49 opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom'))
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50
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51 # Check parameter values
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52
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53 if ( ! file.exists(opt$input_loom)){
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54 stop((paste('File', opt$input_loom, 'does not exist')))
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55 }
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56
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57 # Filter out unexpressed features
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58
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59 scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment')
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60 print(paste("Starting with", ncol(scle), "cells and", nrow(scle), "features."))
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61
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62 # Create a logical vector of features that are expressed (above detection_limit)
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63 feature_expressed <- nexprs(scle, detection_limit = opt$detection_limit, exprs_values = 1, byrow=TRUE) > 0
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64 scle <- scle[feature_expressed, ]
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65
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66 print(paste("After filtering out unexpressed features: ", ncol(scle), "cells and", nrow(scle), "features."))
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67
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68 # Filter low library sizes
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69 to_keep <- scle$total_counts > opt$library_size
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70 scle <- scle[, to_keep]
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71
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72 print(paste("After filtering out low library counts: ", ncol(scle), "cells and", nrow(scle), "features."))
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73
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74 # Filter out high MT counts
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75 to_keep <- scle$pct_counts_MT < opt$percent_counts_MT
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76 scle <- scle[, to_keep]
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77
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78 print(paste("After filtering out high MT gene counts: ", ncol(scle), "cells and", nrow(scle), "features."))
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79
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80 # Output to a Loom file
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81 if (file.exists(opt$output_loom)) {
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82 file.remove(opt$output_loom)
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83 }
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84 export(scle, opt$output_loom, format='loom')