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1 <tool id="deseq2" name="DESeq2" version="2.0.1">
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2 <description>Determines differentially expressed features from count data</description>
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3 <requirements>
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4 <requirement type="binary">Rscript</requirement>
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5 <requirement type="R-module">DESeq2</requirement>
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6 <requirement type="package" version="3.0.1">R_3_0_1</requirement>
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7 <requirement type="package" version="1.0.17">deseq2</requirement>
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8 <!--<requirement type="set_environment">DESEQ2_SCRIPT_PATH</requirement>-->
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9 </requirements>
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10 <command interpreter="Rscript">
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11 #import simplejson
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12 deseq2.R
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13 -o "$deseq_out"
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14 --outfilefiltered "$deseq_out_filtered"
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14
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15
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16 #if $pdf:
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17 -p "$plots"
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14
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18 #end if
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19
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20 -i "$input_matrix"
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21
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22 #set $temp_factor_name = list()
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23 #for $factor in $rep_factorName:
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24 #set $temp_factor = dict()
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25 #for $level in $factor.rep_factorLevel:
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26 ##$temp_factor_list.append( '%s::%s:%s' % ($factor.factorName.replace(' ','_'), $level.factorLevel, $level.factorIndex) )
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27 $temp_factor.update({str($level.factorLevel): map(int, str($level.factorIndex).split(','))})
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28 #end for
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29 $temp_factor_name.append([str($factor.factorName), $temp_factor])
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30
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31 #end for
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32
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33 ##-m "#echo ' '.join( $temp_factor_list )#"
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34 -m '#echo simplejson.dumps(temp_factor_name)#'
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35 ##--organism "$organism"
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36 ##-t "$fittype"
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37 -c $countthreshold
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38 </command>
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39 <stdio>
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40 <regex match="Execution halted"
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41 source="both"
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42 level="fatal"
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43 description="Execution halted." />
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44 <regex match="Input-Error 01"
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45 source="both"
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46 level="fatal"
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47 description="Error in your input parameters: Make sure you only apply factors to selected samples." />
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48 <regex match="Error in"
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49 source="both"
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50 level="fatal"
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51 description="An undefined error occured, please check your intput carefully and contact your administrator." />
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52 </stdio>
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53 <inputs>
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54 <param format="tabular" name="input_matrix" type="data" label="Countmatrix" help="You can create a count matrix with the tool
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55 'Count reads in features with htseq-count'"/>
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56
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57 <repeat name="rep_factorName" title="Factor/Condition" min="1">
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58 <param name="factorName" type="text" value="FactorName" label="Specify a factor name" help=""/>
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59 <repeat name="rep_factorLevel" title="Factor level" min="1">
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60 <param name="factorLevel" type="text" value="FactorLevel" label="Specify a factor level" help=""/>
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61
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62 <param name="factorIndex" label="Select columns that are associated with this factor level" type="data_column" data_ref="input_matrix"
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63 numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes">
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64 <validator type="no_options" message="Please select at least one column."/>
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65 </param>
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66 </repeat>
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67 </repeat>
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68 <!--
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69 <param name="control_cols" type="select" display="checkboxes" multiple="true" optional="True" label="Select columns containing first condition"
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70 dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="insert useful info here">
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71 <validator type="no_options" message="Please select at least one column."/>
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72 </param>
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73 <param name="experiement_cols" type="select" display="checkboxes" multiple="true" optional="True" label="Select columns containing second condition"
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74 dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="insert useful info here">
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75 <validator type="no_options" message="Please select at least one column."/>
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76 </param>
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77
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78 <repeat name="factor" title="factor">
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79 <param name="factor_name" type="text" value="Factor Name" label="Specify a factor name"
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80 help="Order of factors you add effects the design formual and hence effects whole analysis. Design formual will be created as follows: ~factor1+factor2+factor3+...+factorN+condition"/>
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81 <param name="factor_index" type="select" display="checkboxes" multiple="true" optional="True" label="Choose sample to compare with"
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82 dynamic_options="get_matrix_header( input_dataset=input_matrix )" help="Select columns that are associated with a factor">
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83 <validator type="no_options" message="Please select at least one column."/>
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84 </param>
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85 </repeat>
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86 -->
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87 <!--param name="organism" size="10" type="select">
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88 <option value="human">human</option>
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89 <option value="mouse">mouse</option>
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90 <option value="fly">fly</option>
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91 <option value="other">other</option>
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92 </param-->
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93 <param name="countthreshold" size="10" type="float" value="10" label="Filter out features with mean normalized counts lower than this value"/>
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94 <param name="fittype" type="select" label="Type of fitting of dispersions to the mean intensity">
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95 <option value="parametric">parametric</option>
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96 <option value="local">local</option>
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97 <option value="mean">mean</option>
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98 </param>
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99 <param name="pdf" type="boolean" truevalue="" falsevalue="" checked="true" label="Visualising the analysis results"
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100 help="output an additional PDF files" />
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101 </inputs>
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102
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103 <outputs>
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104 <data format="tabular" name="deseq_out" label="DESeq2 result file on ${on_string}"/>
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105 <data format="tabular" name="deseq_out_filtered" label="Independent filtering result file on ${on_string}"/>
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106 <data format="pdf" name="plots" label="DESeq2 plots on ${on_string}">
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107 <filter>pdf == True</filter>
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108 </data>
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109 </outputs>
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110 <code file="matrix_helper.py" />
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111
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112 <help>
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113
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114 .. class:: infomark
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115
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116 **What it does**
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117
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118 Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution
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119
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120
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121 **Inputs**
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122
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123 DESeq2_ requires one count matrix as input file. You can use the tool
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124
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125
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126
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127 **Output**
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128
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129 DESeq2_ generates a tabular file containing the different columns and optional visualized results as PDF.
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130
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131 ====== ==========================================================
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132 Column Description
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133 ------ ----------------------------------------------------------
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134 1 Gene Identifiers
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135 2 mean normalised counts, averaged over all samples from both conditions
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136 3 the logarithm (to basis 2) of the fold change
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137 4 standard error estimate for the log2 fold change estimate
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138 5 p value for the statistical significance of this change
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139 6 p value adjusted for multiple testing with the Benjamini-Hochberg procedure
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140 which controls false discovery rate (FDR)
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141 ====== ==========================================================
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142
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143
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144 ------
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145
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146 **References**
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147
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148 DESeq2_ Authors: Michael Love (MPIMG Berlin), Simon Anders, Wolfgang Huber (EMBL Heidelberg)
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149
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150 If _DESeq2_ is used to obtain results for scientific publications it
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151 should be cited as [1]_. A paper describing DESeq2_ is in preparation.
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152
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153
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154
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155 .. [1] Anders, S and Huber, W (2010): `Differential expression analysis for sequence count data`_.
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156
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157 .. _Differential expression analysis for sequence count data: http://dx.doi.org/10.1186/gb-2010-11-10-r106
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158 .. _DESeq2: http://master.bioconductor.org/packages/release/bioc/html/DESeq2.html
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159
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160
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161 </help>
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162 </tool>
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