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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/decontam commit 8eca6c026e7bd7ab2e471485cb93b8dee1cd2c07
author iuc
date Sun, 09 Mar 2025 19:27:13 +0000
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86da5c894956 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/decontam commit 9e0ce6d02ee71d5b974ef615b1c5286bc45d8e6b
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1 <tool id="decontam" name="Decontam" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
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2 <description>Identification and removal of contaminants</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="bio_tools"/>
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7 <expand macro="requirements"/>
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8 <command detect_errors="exit_code"><![CDATA[
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9 Rscript '$rscript'
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10 ]]></command>
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11 <configfiles>
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12 <configfile name="rscript"><![CDATA[
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13 library(tidyverse)
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14 library(phyloseq)
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15 library(ggplot2)
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16 library(decontam)
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17
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18 #if $input_type.select_input == 'phyloseq':
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19 ps <- readRDS("$input_type.phyloseq_object")
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20 sample_data(ps)\$control <- as.logical(sample_data(ps)[["$input_type.control_metadata"]])
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21 #else
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22 ## get OTU table (first column is the OTU/ASV ID)
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23 otu <- read_tsv("$input_type.otu")
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24 otu2 <- otu %>% tibble::column_to_rownames(colnames(otu)[1]) #use first column as rownames
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25 OTU <- otu_table(otu2, taxa_are_rows = FALSE)
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27 ## get metadata table must have matching OTU/ASV ID in first column
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28 meta <- read_tsv("$input_type.metadata")
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29 meta2 <- meta %>% tibble::column_to_rownames(colnames(meta)[1]) #use first column as rownames
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31 control_column = as.integer("$input_type.control") - 1 ##remove one index since the dataframe uses the first column as index
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33 ## convert 0/1 to bool for the control column and store in control column
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34 meta2\$control <- as.logical(meta2[[control_column]])
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35 sampledata <- sample_data(meta2)
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36
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37 ps <- phyloseq(OTU, FALSE, sampledata)
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38
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39 #end if
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41 ## plot library_size_vs_control
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42 df <- as.data.frame(sample_data(ps)) # Put sample_data into a ggplot-friendly data.frame
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43 df\$LibrarySize <- sample_sums(ps)
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44 df <- df[order(df\$LibrarySize),]
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45 df\$Index <- seq(nrow(df))
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46 ggplot(data=df, aes(x=Index, y=LibrarySize, color=control)) + geom_point()
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47 ggsave("$library_size_vs_control", device = "png", width = 10, height = 8, units = "cm")
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48
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49 ## plot prevalence
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50 contamdf.prev <- isContaminant(ps, method="prevalence", neg="control", threshold=$threshold)
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51 table(contamdf.prev\$contaminant)
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52
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53 ps.pa <- transform_sample_counts(ps, function(abund) 1*(abund>0))
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54 ps.pa.neg <- prune_samples(sample_data(ps.pa)\$control == TRUE, ps.pa)
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55 ps.pa.pos <- prune_samples(sample_data(ps.pa)\$control == FALSE, ps.pa)
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56
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57 ## Make data.frame of prevalence in positive and negative samples
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58 df.pa <- data.frame(pa.pos=taxa_sums(ps.pa.pos), pa.neg=taxa_sums(ps.pa.neg),
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59 contaminant=contamdf.prev\$contaminant)
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60 ggplot(data=df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
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61 xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
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62 ggsave("$prevalence", device = "png", width = 10, height = 8, units = "cm")
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63
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64 ## remove contamination features from original data
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65 #if $input_type.select_input == 'phyloseq':
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66 id_name <- "SampleID"
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67 #else
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68 id_name <- colnames(otu)[1] ## we use the same name for the ID column as the OTU input
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69 #end if
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70
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71 ps.noncontam <- prune_taxa(!contamdf.prev\$contaminant, ps)
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72
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73 otu_table(ps.noncontam) %>%
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74 as.data.frame() %>%
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75 rownames_to_column(id_name) -> otu
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76
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77 write.table(otu,
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78 file="$decontam_otu",
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79 sep = "\t",
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80 row.names=FALSE,
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81 quote = FALSE)
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82
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83 saveRDS(ps.noncontam, "$decontam_phyloseq")
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84
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85 ]]></configfile>
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86 </configfiles>
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87 <inputs>
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88 <conditional name="input_type">
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89 <param name="select_input" type="select" label="Phyloseq or Feature table input" help="This tool can work with phyloseq objects or feature table inputs.">
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90 <option value="phyloseq">Phyloseq</option>
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91 <option value="feature_table">Feature table</option>
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92 </param>
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93 <when value="phyloseq">
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94 <param name="phyloseq_object" type="data" format="phyloseq" label="Phyloseq object"/>
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95 <param name="control_metadata" type="text" label="Control column" help="Column in the phyloseq metadata specifying weather a sample is a negative control (0 for normal samples / 1 for control)"/>
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96 </when>
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97 <when value="feature_table">
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98 <param name="otu" type="data" format="tabular" label="Feature table" help="OTU/ASV or other feature table. The first column must have corresponding IDs to the metadata table."/>
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99 <param name="metadata" type="data" format="tabular" label="Metadata" help="Metadata that contains a column specifying weather a samples is a negativ control (0 for normal samples / 1 for control). The first column must have corresponding IDs to the feature table."/>
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100 <param name="control" type="data_column" data_ref="metadata" use_header_names="true" multiple="false" optional="false" label="Control column" help="Column specifying weather a sample is a negative control (0 for normal samples / 1 for control)."/>
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101 </when>
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102 </conditional>
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103 <param name="threshold" type="float" label="Threshold to detect a contaminant" value="0.1" min="0" max="1" help="Probability of the feature to be a decontaminant in the statistical test being performed." />
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104 </inputs>
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105 <outputs>
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106 <data name="library_size_vs_control" format="png" label="${tool.name} on ${on_string}: Library Size vs Control Plot"/>
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107 <data name="prevalence" format="png" label="${tool.name} on ${on_string}: Prevalence Plot"/>
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108 <data name="decontam_otu" format="tabular" label="${tool.name} on ${on_string}: Removed Contaminants - Feature Table"/>
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109 <data name="decontam_phyloseq" format="phyloseq" label="${tool.name} on ${on_string}: Removed Contaminants - Phyloseq Object"/>
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110 </outputs>
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111 <tests>
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112 <test>
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113 <conditional name="input_type">
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114 <param name="select_input" value="phyloseq"/>
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115 <param name="phyloseq_object" value="phyloseq_input.rds"/>
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116 <param name="control_metadata" value="control"/>
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117 </conditional>
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118 <param name="threshold" value="0.5"/>
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119 <output name="decontam_phyloseq" file="phyloseq_output.rds" ftype="phyloseq"/>
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120 <output name="decontam_otu" file="otu_output.tsv" ftype="tabular"/>
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121 <output name="prevalence" file="Prevalence_Plot.png" ftype="png"/>
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122 <output name="library_size_vs_control" file="Library_Size_vs_Control_Plot.png" ftype="png"/>
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123 </test>
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124 <test>
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125 <conditional name="input_type">
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126 <param name="select_input" value="feature_table"/>
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127 <param name="otu" value="otu_input.tsv"/>
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128 <param name="metadata" value="metadata_input.tsv"/>
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129 <!-- using the index of the column -->
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130 <param name="control" value="8"/>
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131 </conditional>
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132 <param name="threshold" value="0.5"/>
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133 <output name="decontam_phyloseq" file="phyloseq_output2.rds" ftype="phyloseq"/>
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134 <output name="decontam_otu" file="otu_output.tsv" ftype="tabular"/>
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135 <output name="prevalence" file="Prevalence_Plot.png" ftype="png"/>
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136 <output name="library_size_vs_control" file="Library_Size_vs_Control_Plot.png" ftype="png"/>
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137 </test>
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138 </tests>
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139 <help><![CDATA[
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140 Simple Statistical Identification of Contaminating Sequence Features in Marker-Gene or Metagenomics Data
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141 ========================================================================================================
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142
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143 This tool identifies contaminating sequence features in marker-gene or
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144 metagenomics datasets. It can be applied to any type of feature derived from
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145 environmental sequencing data (e.g., ASVs, OTUs, taxonomic groups, MAGs,
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146 etc.). The method requires either DNA quantitation data or sequenced negative
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147 control samples.
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148
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149 .. note::
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150
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151 Currently, only the negative control sample method is implemented in this
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152 wrapper.
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153
3
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154 **Method**
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155
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156 To identify contaminants decontam requires a defined set of “negative control” samples in
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157 which sequencing was performed on blanks without any biological sample added.
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158 Extraction controls are preferred, and in amplicon sequencing the negative controls
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159 should also be carried through the PCR step, as each step in the workflow
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160 has the potential to introduce new contaminants.
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161
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162 Decontam then uses Fisher’s exact tests to detect weather a feature is a contaminant or not,
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163 by comparing true samples to ngeative controls. For more details on the detection method see
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164 the publication of decontam.
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165
0
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166 **Output**
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167
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168 - If a phyloseq object is provided as input, the output will be a phyloseq
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169 object pruned to include only non-contaminant features.
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170 - If only the feature table or metadata is provided, the output will be a
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171 pruned phyloseq object containing only non-contaminant features, without
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172 the TAX table. The output feature table will also be pruned to include only
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173 non-contaminant features.
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174
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175 **Threshold**
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176
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177 The default threshold for identifying a contaminant is a probability of 0.1
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178 in the statistical test being performed.
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179
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180 In the prevalence test, a special threshold value of 0.5 is notable: this
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181 will identify as contaminants any sequences that are more prevalent in
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182 negative controls than in positive samples.
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183 ]]>
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184 </help>
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185 <expand macro="citations"/>
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186 </tool>