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author | lgueguen |
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date | Fri, 22 Jul 2016 05:39:13 -0400 |
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children | d86ccac2a660 |
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<tool id="sartools_edger" name="SARTools edgeR" version="1.0.0"> <!-- [REQUIRED] Tool description displayed after the tool name --> <description>Compare two or more biological conditions in a RNA-Seq framework with edgeR</description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements"/> <expand macro="stdio"/> <!-- [REQUIRED] The command to execute --> <command interpreter="python"><![CDATA[ abims_sartools_edger_wrapper.py ## parameters @COMMAND_BASIC_PARAMETERS@ #if str( $advanced_parameters.adv_param ) == "show": @COMMAND_BATCH_PARAM@ --alpha $advanced_parameters.alpha --pAdjustMethod $advanced_parameters.pAdjustMethod --cpmCutoff $advanced_parameters.cpmCutoff --geneSelection $advanced_parameters.geneSelection --normalizationMethod $advanced_parameters.normalizationMethod --colors $advanced_parameters.colors #end if ## ouputs @COMMAND_OUTPUTS@ ]]></command> <!-- [REQUIRED] Input files and tool parameters --> <inputs> <expand macro="basic_parameters" /> <conditional name="advanced_parameters" > <param name="adv_param" type="select" label="Advanced Parameters" help="" > <option value="hide" selected="true">Hide</option> <option value="show">Show</option> </param> <when value="hide" /> <when value="show"> <expand macro="batch_param" /> <expand macro="alpha_param" /> <expand macro="padjustmethod_param" /> <param name="cpmCutoff" type="integer" value="1" min="0" label="Counts-per-million cut-off to filter low counts" help="(-m, --cpmCutoff) Set to 0 to disable filtering. Default is 1." /> <param name="geneSelection" type="select" label="Selection of the features in MDSPlot" help="(-g, --gene.selection) Default is 'pairwise'." > <option value="pairwise" selected="true">pairwise</option> <option value="common">common</option> </param> <param name="normalizationMethod" type="select" label="Normalization method in calcNormFactors" help="(-n, --normalizationMethod) 'TMM' (default), 'RLE' (DESeq method) or 'upperquartile'." > <option value="TMM" selected="true">TMM</option> <option value="RLE">RLE</option> <option value="upperquartile">upperquartile</option> </param> <expand macro="colors_param" /> </when> </conditional> </inputs> <!-- [REQUIRED] Output files --> <outputs> <expand macro="outputs" /> </outputs> <!-- [OPTIONAL] Tests to be run manually by the Galaxy admin --> <tests> <!-- [HELP] Test files have to be in the ~/test-data directory --> <test> <!-- Test with 2 conditions, 2 replicates, 10 features --> <param name="targetFile" dbkey="?" value="target_small.txt" /> <param name="rawDir" value="raw_small.zip" dbkey="?" ftype="zip"/> <param name="adv_param" value="show"/> <output name="log"> <assert_contents> <has_text text="KO vs WT 5 4 9" /> <has_text text="HTML report created" /> </assert_contents> </output> </test> <!-- <test> --> <!-- NOT WORKING YET: Test with 3 conditions, 3 replicates, 10 features, with batch effect --> <!-- <param name="targetFile" dbkey="?" value="targetT048_small.txt" /> <param name="rawDir" value="rawT048_small.zip" dbkey="?" ftype="no_unzip.zip"/> <param name="condRef" value="T0"/> <param name="adv_param" value="show"/> <param name="condition" value="true"/> <output name="tables_html" file="SARTools_edgeR_targetT048_small_tables.html" lines_diff="12"> <extra_files type="file" name="T4vsT0.complete.txt" value="SARTools_edgeR_T4vsT0_small.complete.txt"/> <extra_files type="file" name="T8vsT0.complete.txt" value="SARTools_edgeR_T8vsT0_small.complete.txt"/> <extra_files type="file" name="T8vsT4.complete.txt" value="SARTools_edgeR_T8vsT4_small.complete.txt"/> </output> </test> --> <test> <!-- Test with 2 conditions, 2 replicates, 8217 features --> <param name="targetFile" dbkey="?" value="target.txt" /> <param name="rawDir" value="raw.zip" dbkey="?" ftype="zip"/> <param name="adv_param" value="show"/> <output name="log"> <assert_contents> <has_text text="KO vs WT 2691 2713 5404" /> <has_text text="HTML report created" /> </assert_contents> </output> </test> <!-- <test> --> <!-- NOT WORKING YET: Test with 3 conditions, 3 replicates, 10160 features, with batch effect --> <!-- <param name="targetFile" dbkey="?" value="targetT048.txt" /> <param name="rawDir" value="rawT048.zip" dbkey="?" ftype="no_unzip.zip"/> <param name="condRef" value="T0"/> <param name="adv_param" value="show"/> <param name="condition" value="true"/> <output name="tables_html" file="SARTools_edgeR_targetT048_tables.html" lines_diff="14"> <extra_files type="file" name="T4vsT0.complete.txt" value="SARTools_edgeR_T4vsT0.complete.txt"/> <extra_files type="file" name="T8vsT0.complete.txt" value="SARTools_edgeR_T8vsT0.complete.txt"/> <extra_files type="file" name="T8vsT4.complete.txt" value="SARTools_edgeR_T8vsT4.complete.txt"/> </output> </test> --> </tests> <!-- [OPTIONAL] Help displayed in Galaxy --> <help><![CDATA[ @HELP_AUTHORS@ ============== SARTools edgeR ============== ----------- Description ----------- @HELP_DESCRIPTION@ ----------- Input files ----------- @HELP_INPUT_FILES@ ---------- Parameters ---------- @HELP_BASIC_PARAMETERS@ * **batch:** adjustment variable to use as a batch effect, must be a column of the target file (NULL if no batch effect needs to be taken into account); * **alpha:** significance threshold applied to the adjusted p-values to select the differentially expressed features (default is 0.05); * **pAdjustMethod:** p-value adjustment method for multiple testing [4, 5] ("BH" by default, "BY" or any value of p.adjust.methods); * **cpmCutoff:** counts-per-million cut-off to filter low counts (default is 1, set to 0 to disable filtering); * **gene.selection:** method of selection of the features for the MultiDimensional Scaling plot ("pairwise" by default or common); * **normalizationMethod:** normalization method in calcNormFactors(): "TMM" (default), "RLE" (DESeq method) or "upperquartile"; * **colors:** colors used for the figures (one per biological condition), 8 are given by default. ------------ Output files ------------ @HELP_OUTPUT_FILES@ --------------------------------------------------- [1] G.-K. Smyth. Limma: linear models for microarray data. In R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, and W. Huber, editors, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pages 397–420. Springer, New York, 2005. [2] S. Anders. HTSeq: Analysing high-throughput sequencing data with Python. http://www-huber.embl.de/users/anders/HTSeq/, 2011. [3] S. Anders, P.-T. Pyl, and W. Huber. HTSeq - A Python framework to work with high-throughput sequencing data. bioRxiv preprint, 2014. URL: http://dx.doi.org/10.1101/002824. [4] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57:289–300, 1995. [5] Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29(4):1165–1188, 2001. ]]></help> <citations> <expand macro="common_citations" /> </citations> </tool>