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"planemo upload for repository https://github.com/geraldinepascal/FROGS-wrappers/ commit 2024a13846ea6f9bd94ae62e3b2a5a3aba8cd304-dirty"
author | frogs |
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date | Tue, 24 Aug 2021 08:21:23 +0000 |
parents | 094a2469204d |
children | 445b04a65ed8 |
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<?xml version="1.0"?> <!-- # Copyright (C) 2017 INRA # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. --> <tool id="FROGSSTAT_DESeq2_Preprocess" name="FROGSSTAT DESeq2 Preprocess" version="@TOOL_VERSION@+galaxy2"> <description>import a Phyloseq object and prepare it for DESeq2 differential abundance analysis </description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements_phyloseq"> <requirement type="package" version="1.30.1">bioconductor-deseq2</requirement> <requirement type="package" version="1.6.6">r-optparse</requirement> </expand> <stdio> <exit_code range="1:" /> <exit_code range=":-1" /> </stdio> <command>deseq2_preprocess.py --data '$data' #if $multiple.seconde --var '$var1${multiple.mod}${multiple.var2}' #else --var '$var1' #end if --out-Rdata '$dds' </command> <inputs> <!-- Files --> <param format="rdata" name="data" type="data" label="Phyloseq object (format rdata)" help="This is the result of FROGSSTAT Phyloseq Import Data with normalise option set to NO (DESeq2 is more powerful on unnormalised counts)." optional="false"> <validator type="empty_field" message="This parameter is required." /> </param> <!-- Parameters --> <param name="var1" type="text" label="Experimental variable" help="The factor suspected to have an effect on OTUs' abundances. Ex: Treatment, etc." optional="false" value="" size="20"> <validator type="empty_field" message="This parameter is required." /> </param> <conditional name="multiple"> <param name="seconde" type="boolean" label="Do you want to correct for a confounding factor?" help="If yes, specifiy counfouding factor." optional="false" /> <when value="false"></when> <when value="true"> <param name="var2" type="text" label="Variable 2" help="Confounding factor. Ex: Gender, etc." optional="false" value="" size="20"/> <param name="mod" type="select" multiple="false" help="Choose an additive model (+) if the effects of Treatment and Gender are independent. Choose a model with interaction (*) if the effect of Treatment is expected to depend on Gender." label="Type of model"> <option value="+" selected="true">Linear (+)</option> <option value="*">Interaction (*)</option> </param> </when> </conditional> </inputs> <outputs> <data format="rdata" name="dds" label="${tool.name}: dds.Rdata" from_work_dir="DESeq2_preprocess.Rdata"/> </outputs> <tests> <test> <param name="data" value="references/16-phylo_import.Rdata" /> <param name="var1" value="EnvType"/> <output name="dds" file="references/23-deseq2_preprocess.Rdata" compare="sim_size" delta="50"/> </test> </tests> <help> @HELP_LOGO@ .. class:: infomark page-header h2 What it does Launch Rscript to compute differential abundance analysis using `DESeq2 <https://bioconductor.org/packages/release/bioc/html/DESeq2.html>`_ on a `Phyloseq <https://joey711.github.io/phyloseq/>`_ object. .. class:: infomark page-header h2 Inputs/Outputs .. class:: h3 Input **phyloseq object** (format rdata): A phyloseq object stored in a rdata file. This file is the result of FROGSSTAT Phyloseq Import Data. .. class:: warningmark We need unnormalised data to analyse the abundance differencies. .. class:: h3 Ouput **dds object** (format rdata): A DESeq2 dataset (dds) stored in rdata file. This result will be one of the input of the FROGSSTAT DESeq Visualisation tool. .. class:: infomark page-header h2 How it works The DESeq function performs a default analysis through the steps: 1. estimation of size factors: ‘estimateSizeFactors’ 2. estimation of dispersion: ‘estimateDispersions’ 3. Negative Binomial GLM fitting and Wald statistics: ‘nbinomWaldTest’ You must precise at least on variable on which the differential abundances will be compared. You may precise a confounding factor, by adding an other variable and precise the relation between the two variables: - + for independant variables - * for interacting variables @HELP_CONTACT@ </help> <citations> <expand macro="citations" /> </citations> </tool>