annotate edgeR_Convert_DGE_Table_to_Bedgraph.xml @ 1:a4a4c88783ea draft

planemo upload for repository https://bitbucket.org/EMCbioinf/galaxy-tool-shed-tools/raw/master/edger_with_design_matrix commit 2700e500a4fb135a20ede7d52221a9d31f1aaa5e-dirty

<?xml version="1.0" encoding="UTF-8"?>
    <tool id="edger_dge_table_to_bedgraph" name="edgeR: Convert 'differentially expressed genes'-table to bedgraph(s)" version="1.0.0">
    <description>EdgeR's "differentially expressed genes" table to bedgraph(s)</description>
    
    <macros>
        <import>edgeR_macros.xml</import>
    </macros>
    
    <requirements>
        <requirement type="package" version="1.0.0">edger_dge_table_to_bedgraph</requirement>
    </requirements>
    
    <command interpreter="python">
        edger_dge_table_to_bedgraph
            -t $cpm_table
            -g $geneset
            
            #if $logfc:
                -c3 $logfc
            #end if
            
            #if $logcpm:
                -c4 $logcpm
            #end if
            
            #if $lr:
                -c5 $lr
            #end if
            
            #if $pvalue:
                -c6 $pvalue
            #end if
            
            #if $fdr:
                -c7 $fdr
            #end if
    </command>
    
    <inputs>
        <param format="tabular" name="cpm_table" type="data" label="'differentially expressed genes'-table as result from EdgeR" help="must have 7 columns of which the 2nd are gene names matching the GTF file" />
        <param format="gtf,gff,gff3" name="geneset" type="data" label="Geneset used for estimating expression levels prior to expression analysis" />
    
        <param name="columns" type="select" label="Desired columns" multiple="true" display="checkboxes">
            <option value="c3" selected="true">logFC</option>
            <option value="c4">logCPM</option>
            <option value="c5">LR</option>
            <option value="c6">PValue</option>
            <option value="c7" selected="true">FDR</option>
        </param>
    </inputs>
    
    <outputs>
        <data format="bedgraph" name="logfc" label="logFC from ${cpm_table.name}">
            <filter>"c3" in columns</filter>
        </data>
        
        <data format="bedgraph" name="logcpm" label="logCPM from ${cpm_table.name}">
            <filter>"c4" in columns</filter>
        </data>
        
        <data format="bedgraph" name="lr" label="LR from ${cpm_table.name}">
            <filter>"c5" in columns</filter>
        </data>
        
        <data format="bedgraph" name="pvalue" label="PValue from ${cpm_table.name}">
            <filter>"c6" in columns</filter>
        </data>
        
        <data format="bedgraph" name="fdr" label="FDR from ${cpm_table.name}">
            <filter>"c7" in columns</filter>
        </data>
    </outputs>
    
    <tests>
        <test>
            <param name="cpm_table" value="Convert_DGE_Table_to_Bedgraph/table_01.tabular.txt" />
            <param name="geneset" value="Convert_DGE_Table_to_Bedgraph/genes_01.gtf" />
            
            <param name="columns" value="c3,c7" />
            
            <output name="logfc" file="Convert_DGE_Table_to_Bedgraph/logFC.output.bedgraph" />
            <output name="fdr" file="Convert_DGE_Table_to_Bedgraph/FDR.output.bedgraph" />
        </test>
    </tests>
    
    <help>
        P-values and FDRs are swapped from 1 to 0, and 0 to 1, because this way the most siginificant genes will obtain the highest values which is convenient for visualisation.
        
        @CONTACT@
    </help>
    
    <expand macro="citations" />
</tool>