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1 <tool id="QCnormalization" name="QCnormalization" version="0.1.0">
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2 <description>Quality control and normalization of affymetrix expression data</description>
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3 <requirements>
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4 <!--container type="docker">sblanck/smat</container-->
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5 <requirement type="package" version="0.1.0">r-smagexp</requirement>
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6 </requirements>
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7 <command interpreter="python">
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8 <![CDATA[
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9 stderr_wrapper.py Rscript
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10 ${__tool_directory__}/AffyQCnormalization.R
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11 #for $input in $inputs
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12 "${input}"
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13 "${input.name}"
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14 #end for
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15 "${normalization}"
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16 $result_export_eset
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17 $result_html
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18 $result_html.files_path
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19 /${__tool_directory__}/AffyQCnormalization_tpl.html
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20 ]]>
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21 </command>
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22
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23 <inputs>
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24
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25 <param name="inputs" type="data" format="cel" multiple="true" label=".CEL files" help=".CEL files to be used"/>
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26
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27 <param name="normalization" type="select" label="Preprocessing/normalization">
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28
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29 <option value="rma">rma (backgroung correction + quantile normalization + log2)</option>
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30 <option value="quantile">quantile normalization + log2</option>
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31 <option value="background">background correction + log2</option>
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32 <option value="log2">log2 only</option>
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33
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34
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35 </param>
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36
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37 </inputs>
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38 <outputs>
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39 <data format="rdata" name="result_export_eset" label="export normalized expressionSet"/>
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40 <data format="html" name="result_html" label="QC result"/>
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41 </outputs>
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42
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43 <help>
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44 <![CDATA[
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45 **What it does**
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46
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47 The QCnormalization tool offers to ensure the quality of the data and to normalize them. Several normalization methods are available :
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48
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49 * rma normalization
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50 * quantile normalization + log2
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51 * background correction + log2
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52 * log2 only
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53
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54 **Results**
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55
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56 - Several quality figures : microarray images, boxplots and MA plots
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57 - Rdata object containing the normalized data for further analysis
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58
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59 ]]>
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60 </help>
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61
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62 </tool>
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