Mercurial > repos > iuc > edger

<tool id="edger" name="edgeR" version="3.20.7.0">
    <description>
        Perform differential expression of count data
    </description>

    <requirements>
        <requirement type="package" version="3.20.7">bioconductor-edger</requirement>
        <requirement type="package" version="0.2.15">r-rjson</requirement>
        <requirement type="package" version="1.20.0">r-getopt</requirement>
        <requirement type="package" version="1.4.30">r-statmod</requirement>
        <!-- required for alpha function used with plotMD -->
        <requirement type="package" version="0.5.0">r-scales</requirement>
    </requirements>

    <version_command><![CDATA[
echo $(R --version | grep version | grep -v GNU)", edgeR version" $(R --vanilla --slave -e "library(edgeR); cat(sessionInfo()\$otherPkgs\$edgeR\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", scales version" $(R --vanilla --slave -e "library(scales); cat(sessionInfo()\$otherPkgs\$scales\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", rjson version" $(R --vanilla --slave -e "library(rjson); cat(sessionInfo()\$otherPkgs\$rjson\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", getopt version" $(R --vanilla --slave -e "library(getopt); cat(sessionInfo()\$otherPkgs\$getopt\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
    ]]></version_command>

    <command detect_errors="exit_code"><![CDATA[
#import json
Rscript '$__tool_directory__/edger.R'

-R '$outReport'
-o '$outReport.files_path'

#if $input.format=="files":

    ## Adapted from DESeq2 wrapper
    #set $temp_factor_names = list()
    #for $fact in $input.rep_factor:
        #set $temp_factor = list()
        #for $g in $fact.rep_group:
            #set $count_files = list()
            #for $file in $g.countsFile:
                $count_files.append(str($file))
            #end for
            $temp_factor.append( {str($g.groupName): $count_files} )
        #end for

        $temp_factor.reverse()
        $temp_factor_names.append([str($fact.factorName), $temp_factor])
    #end for
    -j '#echo json.dumps(temp_factor_names)#'

#elif $input.format=="matrix":
    -m '$input.counts'
    #if $input.fact.ffile=='yes':
        -f '$input.fact.finfo'
    #else:
        -i '${ '|'.join( ['%s::%s' % ($x.factorName, $x.groupNames) for x in $input.fact.rep_factor] ) }'
    #end if
#end if

#if $anno.annoOpt=='yes':
    -a '$anno.geneanno'
#end if

-C '${ ','.join( ['%s' % $x.contrast for x in $rep_contrast] ) }'

#if $f.filt.filt_select == 'yes':
    #if $f.filt.cformat.format_select == 'cpm':
        -c '$f.filt.cformat.cpmReq'
        -s '$f.filt.cformat.cpmSampleReq'
    #elif $f.filt.cformat.format_select == 'counts':
            -z '$f.filt.cformat.cntReq'
        #if $f.filt.cformat.samples.count_select == 'total':
            -y
        #elif $f.filt.cformat.samples.count_select == 'sample':
            -s '$f.filt.cformat.samples.cntSampleReq'
        #end if
    #end if
#end if

#if $out.normCounts:
    -x
#end if
#if $out.rdaOption:
    -r
#end if

-l '$adv.lfc'
-p '$adv.pVal'
-d '$adv.pAdjust'
-n '$adv.normalisationOption'
#if $adv.robOption:
    -b
#end if
#if $adv.lrtOption:
    -t
#end if

&&
mkdir ./output_dir

&&
cp '$outReport.files_path'/*.tsv output_dir/
    ]]></command>

    <inputs>

        <!-- Counts and Factors -->
        <conditional name="input">
            <param name="format" type="select" label="Count Files or Matrix?"
                help="You can choose to input either separate count files (one per sample) or a single count matrix">
                <option value="files">Separate Count Files</option>
                <option value="matrix">Single Count Matrix</option>
            </param>

            <when value="files">
                <repeat name="rep_factor" title="Factor" min="1">
                    <param name="factorName" type="text" label="Name" help="Name of experiment factor of interest (e.g. Genotype). One factor must be entered and there must be two or more groups per factor. Optional additional factors (e.g. Batch) can be entered using the Insert Factor button below, see Help section for more information. NOTE: Please only use letters, numbers or underscores.">
                    <sanitizer>
                        <valid initial="string.letters,string.digits"><add value="_" /></valid>
                    </sanitizer>
                    </param>
                    <repeat name="rep_group" title="Group" min="2" default="2">
                        <param name="groupName" type="text" label="Name"
                        help="Name of group that the counts files(s) belong to (e.g. WT or Mut). NOTE: Please only use letters, numbers or underscores (case sensitive).">
                        <sanitizer>
                            <valid initial="string.letters,string.digits"><add value="_" /></valid>
                        </sanitizer>
                        </param>
                        <param name="countsFile" type="data" format="tabular" multiple="true" label="Counts file(s)"/>
                    </repeat>
                </repeat>
            </when>

            <when value="matrix">
                <param name="counts" type="data" format="tabular" label="Count Matrix"/>

                <conditional name="fact">
                    <param name="ffile" type="select" label="Input factor information from file?"
                        help="You can choose to input the factor and group information for the samples from a file or manually enter below.">
                        <option value="no">No</option>
                        <option value="yes">Yes</option>
                    </param>
                    <when value="yes">
                        <param name="finfo" type="data" format="tabular" label="Factor File"/>
                    </when>
                    <when value="no" >
                        <repeat name="rep_factor" title="Factor" min="1">
                            <param name="factorName" type="text" label="Factor Name"
                                help="Name of experiment factor of interest (e.g. Genotype). One factor must be entered and there must be two or more groups per factor. Additional factors (e.g. Batch) can be entered using the Insert Factor button below, see Help section below. NOTE: Please only use letters, numbers or underscores.">
                                <validator type="empty_field" />
                                <validator type="regex" message="Please only use letters, numbers or underscores">^[\w]+$</validator>
                            </param>
                            <param name="groupNames" type="text" label="Groups"
                                help="Enter the group names for the samples separated with commas e.g. WT,WT,WT,Mut,Mut,Mut. The order of the names must match the order of the samples in the columns of the count matrix. NOTE: Please only use letters, numbers or underscores (case sensitive).">
                                <validator type="empty_field" />
                                <validator type="regex" message="Please only use letters, numbers or underscores, and separate levels by commas">^[\w,]+$</validator>
                            </param>
                        </repeat>
                    </when>
                </conditional>
            </when>
        </conditional>

        <!-- Gene Annotations -->
        <conditional name="anno">
            <param name="annoOpt" type="select" label="Use Gene Annotations?"
                    help="If you provide an annotation file, annotations will be added to the table(s) of differential expression results to provide descriptions for each gene. See Help section below.">
                <option value="no">No</option>
                <option value="yes">Yes</option>
            </param>
            <when value="yes">
                <param name="geneanno" type="data" format="tabular" label="Gene Annotations"/>
            </when>
            <when value="no" />
        </conditional>

        <!-- Contrasts -->
        <repeat name="rep_contrast" title="Contrast" min="1" default="1">
            <param name="contrast" type="text" label="Contrast of Interest" help="Names of two groups to compare separated by a hyphen e.g. Mut-WT. If the order is Mut-WT the fold changes in the results will be up/down in Mut relative to WT. If you have more than one contrast enter each separately using the Insert Contrast button below. For more info, see Chapter 8 in the limma User's guide: https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf">
                <validator type="empty_field" />
                <validator type="regex" message="Please only use letters, numbers or underscores">^[\w-]+$</validator>
            </param>
        </repeat>

        <!-- Filter Options -->
        <section name="f" expanded="false" title="Filter Low Counts">
            <conditional name="filt">
                <param name="filt_select" type="select" label="Filter lowly expressed genes?" help="Treat genes with very low expression as unexpressed and filter out. See the Filter Low Counts section below for more information. Default: No">
                    <option value="no" selected="true">No</option>
                    <option value="yes">Yes</option>
                </param>
                <when value="yes">
                    <conditional name="cformat">
                        <param name="format_select" type="select" label="Filter on CPM or Count values?" help="It is slightly better to base the filtering on count-per-million (CPM) rather than the raw count values so as to avoid favoring genes expressed in samples sequenced to a higher depth. ">
                            <option value="cpm">CPM</option>
                            <option value="counts">Counts</option>
                        </param>
                        <when value="cpm">
                            <param name="cpmReq" type="float" value="1" min="0" label="Minimum CPM" help="Treat genes with CPM below this value as unexpressed and filter out. See the Filter Low Counts section below for more information."/>
                             <param name="cpmSampleReq" type="integer" value="0" min="0" label="Minimum Samples"
                             help="Filter out all genes that do not meet the Minimum CPM in at least this many samples. See the Filter Low Counts section below for more information."/>
                        </when>
                        <when value="counts">
                            <param name="cntReq" type="integer" value="0" min="0" label="Minimum Count" help="Filter out all genes that do not meet this minimum count. You can choose below to apply this filter to the total count for all samples or specify the number of samples under Minimum Samples. See the Filter Low Counts section below for more information." />
                            <conditional name="samples">
                                <param name="count_select" type="select" label="Filter on Total Count or per Sample Count values?" >
                                    <option value="total">Total</option>
                                    <option value="sample">Sample</option>
                                </param>
                                <when value="total">
                                    <param name="totReq" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Filter on Total Count" help="Apply the Minimum Count filter to genes after summing counts for all samples. See the Filter Low Counts section below for more information." />
                                </when>
                                <when value="sample">
                                    <param name="cntSampleReq" type="integer" value="0" min="0" label="Minimum Samples"
                                    help="Filter out all genes that do not meet the Minimum Count in at least this many samples. See the Filter Low Counts section below for more information."/>
                                </when>
                            </conditional>
                        </when>
                    </conditional>
                </when>
                <when value="no" />
            </conditional>
        </section>

        <!-- Output Options -->
        <section name="out" expanded="false" title="Output Options">
            <param name="normCounts" type="boolean" truevalue="1" falsevalue="0" checked="false"
                label="Output Normalised Counts Table?"
                help="Output a file containing the normalised counts, these are in log2 counts per million (logCPM). Default: No">
            </param>
            <param name="rdaOption" type="boolean" truevalue="1" falsevalue="0" checked="false"
                label="Output RData file?"
                help="Output all the data used by R to construct the plots and tables, can be loaded into R. A link to the RData file will be provided in the HTML report. Default: No">
            </param>
        </section>

        <!-- Advanced Options -->
        <section name="adv" expanded="false" title="Advanced Options">
            <param name="lfc" type="float" value="0" min="0"
                label="Minimum Log2 Fold Change"
                help="Genes above this threshold and below the p-value threshold are considered significant and highlighted in the MD plot. Default: 0."/>
            <param name="pVal" type="float" value="0.05" min="0" max="1"
                label="P-Value Adjusted Threshold"
                help="Genes below this threshold are considered significant and highlighted in the MD plot. If either BH(1995) or BY(2001) are selected then this value is a false-discovery-rate control. If Holm(1979) is selected then this is an adjusted p-value for family-wise error rate. Default: 0.05."/>
            <param name="pAdjust" type="select" label="P-Value Adjustment Method" help="Default: BH">
                <option value="BH" selected="true">Benjamini and Hochberg (1995)</option>
                <option value="BY">Benjamini and Yekutieli (2001)</option>
                <option value="holm">Holm (1979)</option>
                <option value="none">None</option>
            </param>
            <param name="normalisationOption" type="select" label="Normalisation Method" help="Default: TMM">
                <option value="TMM" selected="true">TMM</option>
                <option value="RLE">RLE</option>
                <option value="upperquartile">Upperquartile</option>
                <option value="none">None (Don't normalise)</option>
            </param>
            <param name="robOption" type="boolean" truevalue="1" falsevalue="0" checked="true" label="Use Robust Settings?" help="Using robust settings is usually recommended to protect against outlier genes. Default: Yes" />
            <param name="lrtOption" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Use Likelihood Ratio Test?" help="Use likelihood ratio test instead of quasi-likelihood F-test. Default: No"/>
        </section>

    </inputs>

    <outputs>
        <data name="outReport" format="html" label="${tool.name} on ${on_string}: Report" />
        <collection name="outTables" type="list" label="${tool.name} on ${on_string}: Tables">
            <discover_datasets pattern="(?P&lt;name&gt;.+)\.tsv$" format="tabular" directory="output_dir" visible="false" />
        </collection>
    </outputs>

    <tests>
        <!-- Ensure report is output -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="WT-Mut" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output_collection name="outTables" count="2">
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT.tsv" />
                <element name="edgeR_WT-Mut" ftype="tabular" file="edgeR_WT-Mut.tsv" />
            </output_collection>
            <output name="outReport" >
                <assert_contents>
                    <has_text text="edgeR Analysis Output" />
                    <has_text text="quasi-likelihood" />
                    <not_has_text text="likelihood ratio" />
                    <not_has_text text="RData" />
                </assert_contents>
            </output>
        </test>
        <!-- Ensure annotation file input works -->
        <test>
            <param name="format" value="matrix" />
            <param name="annoOpt" value="yes" />
            <param name="geneanno" value="anno.txt" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output_collection name="outTables" count="1">
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_anno.tsv" />
            </output_collection>
        </test>
        <!-- Ensure RData file can be output -->
        <test>
            <param name="format" value="matrix" />
            <param name="rdaOption" value="true" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output name="outReport" >
                <assert_contents>
                    <has_text text="RData" />
                </assert_contents>
            </output>
        </test>
        <!-- Ensure secondary factors work -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_factor">
                <param name="factorName" value="Batch"/>
                <param name="groupNames" value="b1,b2,b3,b1,b2,b3"/>
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output_collection name="outTables" count="1" >
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_2fact.tsv" />
            </output_collection>
        </test>
        <!-- Ensure factors file input works -->
        <test>
            <param name="format" value="matrix" />
            <param name="ffile" value="yes" />
            <param name="finfo" value="factorinfo.txt" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output_collection name="outTables" count="1">
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_2fact.tsv" />
            </output_collection>
        </test>
        <!-- Ensure normalised counts file output works-->
        <test>
            <param name="format" value="matrix" />
            <param name="normCounts" value="true" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <output_collection name="outTables" count="2">
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT.tsv" />
                <element name="edgeR_normcounts" ftype="tabular" file="edgeR_normcounts.tsv" />
            </output_collection>
        </test>
        <!-- Ensure likelihood ratio option works -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <param name="lrtOption" value="true" />
            <output name="outReport" >
                <assert_contents>
                    <has_text text="likelihood ratio" />
                    <not_has_text text="quasi-likelihood" />
                </assert_contents>
            </output>
        </test>
        <!-- Ensure multiple counts files input works -->
        <test>
            <param name="format" value="files" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <repeat name="rep_group">
                    <param name="groupName" value="WT"/>
                    <param name="countsFile" value="WT1.counts,WT2.counts,WT3.counts"/>
                </repeat>
                <repeat name="rep_group">
                    <param name="groupName" value="Mut"/>
                    <param name="countsFile" value="Mut1.counts,Mut2.counts,Mut3.counts"/>
                </repeat>
            </repeat>
            <repeat name="rep_factor">
                <param name="factorName" value="Batch"/>
                <repeat name="rep_group">
                    <param name="groupName" value="b1"/>
                    <param name="countsFile" value="WT1.counts,Mut1.counts"/>
                </repeat>
                <repeat name="rep_group">
                    <param name="groupName" value="b2"/>
                    <param name="countsFile" value="WT2.counts,Mut2.counts"/>
                </repeat>
                <repeat name="rep_group">
                    <param name="groupName" value="b3"/>
                    <param name="countsFile" value="WT3.counts,Mut3.counts"/>
                </repeat>
            </repeat>
            <param name="annoOpt" value="yes" />
            <param name="geneanno" value="anno.txt" />
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="WT-Mut" />
            </repeat>
            <param name="normCounts" value="true" />
            <output_collection name="outTables" count="3">
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_2fact_anno.tsv" />
                <element name="edgeR_WT-Mut" ftype="tabular" file="edgeR_WT-Mut_2fact_anno.tsv" />
                <element name="edgeR_normcounts" ftype="tabular" file="edgeR_normcounts_anno.tsv" />
            </output_collection>
        </test>
        <!-- Ensure filtering on CPM in Mnimum Samples works -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <param name="filt_select" value="yes" />
            <param name="format_select" value="cpm" />
            <!-- real cpmReq values would be a lot lower
                 this is just for this tiny test dataset -->
            <param name="cpmReq" value="1000" />
            <param name="cpmSampleReq" value="3" />
            <output name="outReport" >
                <assert_contents>
                    <has_text text="CPM in at least" />
                    <not_has_text text="after summing counts for all samples" />
                    <not_has_text text="counts in at least" />
                </assert_contents>
            </output>
            <output_collection name="outTables" count="1" >
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_filt.tsv" />
            </output_collection>
        </test>
        <!-- Ensure filtering on Count in Minmum Samples works -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <param name="filt_select" value="yes" />
            <param name="format_select" value="counts" />
            <param name="cntReq" value="10" />
            <param name="count_select" value="sample" />
            <param name="cntSampleReq" value="3" />
            <output name="outReport" >
                <assert_contents>
                    <has_text text="counts in at least" />
                    <not_has_text text="after summing counts for all samples" />
                    <not_has_text text="CPM in at least" />
                </assert_contents>
            </output>
            <output_collection name="outTables" count="1" >
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_filt.tsv" />
            </output_collection>
        </test>
        <!-- Ensure filtering on Total Count works -->
        <test>
            <param name="format" value="matrix" />
            <param name="counts" value="matrix.txt" />
            <repeat name="rep_factor">
                <param name="factorName" value="Genotype"/>
                <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" />
            </repeat>
            <repeat name="rep_contrast">
                <param name="contrast" value="Mut-WT" />
            </repeat>
            <param name="normalisationOption" value="TMM" />
            <param name="filt_select" value="yes" />
            <param name="format_select" value="counts" />
            <!-- real cntReq values would be a lot lower
                 this is just for this tiny test dataset -->
            <param name="cntReq" value="1000" />
            <param name="count_select" value="total" />
            <param name="totReq" value="true" />
            <output name="outReport" >
                <assert_contents>
                    <has_text text="after summing counts for all samples" />
                    <not_has_text text="counts in at least" />
                    <not_has_text text="CPM in at least" />
                </assert_contents>
            </output>
            <output_collection name="outTables" count="1" >
                <element name="edgeR_Mut-WT" ftype="tabular" file="edgeR_Mut-WT_filt.tsv" />
            </output_collection>
        </test>
    </tests>

    <help><![CDATA[
.. class:: infomark

**What it does**

Given a counts matrix, or a set of counts files, for example from **featureCounts**, and optional information about the genes, this tool
produces plots and tables useful in the analysis of differential gene expression.

This tool uses the `edgeR`_ quasi-likelihood pipeline (edgeR-quasi) for differential expression analysis. This statistical methodology uses negative binomial generalized linear models, but with F-tests instead of likelihood ratio tests. This method provides stricter error rate control than other negative binomial based pipelines, including the traditional edgeR pipelines or DESeq2. While the limma pipelines are recommended for large-scale datasets, because of their speed and flexibility, the edgeR-quasi pipeline gives better performance in low-count situations. For the data analyzed in this `edgeR workflow article`_ ,the edgeR-quasi, limma-voom and limma-trend pipelines are all equally suitable and give similar results.

.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
.. _edgeR workflow article: https://f1000research.com/articles/5-1438

-----

**Inputs**

**Counts Data:**

The counts data can either be input as separate counts files (one sample per file) or a single count matrix (one sample per column). The rows correspond to genes, and columns correspond to the counts for the samples. Values must be tab separated, with the first row containing the sample/column labels and the first column containing the row/gene labels. Gene identifiers can be of any type but must be unique and not repeated within a counts file.

Example - **Separate Count Files**:

    ========== =======
    **GeneID** **WT1**
    ---------- -------
    11287      1699
    11298      1905
    11302      6
    11303      2099
    11304      356
    11305      2528
    ========== =======

Example - **Single Count Matrix**:

    ========== ======= ======= ======= ======== ======== ========
    **GeneID** **WT1** **WT2** **WT3** **Mut1** **Mut2** **Mut3**
    ---------- ------- ------- ------- -------- -------- --------
    11287      1699    1528    1601    1463     1441     1495
    11298      1905    1744    1834    1345     1291     1346
    11302      6       8       7       5        6        5
    11303      2099    1974    2100    1574     1519     1654
    11304      356     312     337     361      397      346
    11305      2528    2438    2493    1762     1942     2027
    ========== ======= ======= ======= ======== ======== ========

**Factor Information:**
Enter factor names and groups in the tool form, or provide a tab-separated file that has the samples in the same order as listed in the columns of the counts matrix. The second column should contain the primary factor levels (e.g. WT, Mut) with optional additional columns for any secondary factors.

Example:

    ========== ============ =========
    **Sample** **Genotype** **Batch**
    ---------- ------------ ---------
    WT1        WT           b1
    WT2        WT           b2
    WT3        WT           b3
    Mut1       Mut          b1
    Mut2       Mut          b2
    Mut3       Mut          b3
    ========== ============ =========

*Factor Name:* The name of the experimental factor being investigated e.g. Genotype, Treatment. One factor must be entered and spaces must not be used. Optionally, additional factors can be included, these are variables that might influence your experiment e.g. Batch, Gender, Subject. If additional factors are entered, edgeR will fit an additive linear model.

*Groups:* The names of the groups for the factor. These must be entered in the same order as the samples (to which the groups correspond) are listed in the columns of the counts matrix. Spaces must not be used and if entered into the tool form above, the values should be separated by commas.


**Gene Annotations:**
Optional input for gene annotations, this can contain more
information about the genes than just an ID number. The annotations will
be available in the differential expression results table and the optional normalised counts table.

Example:

    ==========  ==========  ===================================================
    **GeneID**  **Symbol**  **GeneName**
    ----------  ----------  ---------------------------------------------------
    1287        Pzp         pregnancy zone protein
    1298        Aanat       arylalkylamine N-acetyltransferase
    1302        Aatk        apoptosis-associated tyrosine kinase
    1303        Abca1       ATP-binding cassette, sub-family A (ABC1), member 1
    1304        Abca4       ATP-binding cassette, sub-family A (ABC1), member 4
    1305        Abca2       ATP-binding cassette, sub-family A (ABC1), member 2
    ==========  ==========  ===================================================

**Contrasts of Interest:**
The contrasts you wish to make between levels.
A common contrast would be a simple difference between two levels: "Mut-WT"
represents the difference between the mutant and wild type genotypes.
Multiple contrasts must be entered separately using the Insert Contrast button, spaces must not be used.

**Filter Low Counts:**
Genes with very low counts across all libraries provide little evidence for differential expression.
In the biological point of view, a gene must be expressed at some minimal level before
it is likely to be translated into a protein or to be biologically important. In addition, the
pronounced discreteness of these counts interferes with some of the statistical approximations
that are used later in the pipeline. These genes should be filtered out prior to further
analysis.
As a rule of thumb, genes are dropped if they can’t possibly be expressed in all the samples
for any of the conditions. Users can set their own definition of genes being expressed. Usually
a gene is required to have a count of 5-10 in a library to be considered expressed in that
library. Users should also filter with count-per-million (CPM) rather than filtering on the
counts directly, as the latter does not account for differences in library sizes between samples.

Option to ignore the genes that do not show significant levels of
expression, this filtering is dependent on two criteria: CPM/count and number of samples. You can specify to filter on CPM (Minimum CPM) or count (Minimum Count) values:

    * **Minimum CPM:** This is the minimum count per million that a gene must have in at
      least the number of samples specified under Minimum Samples.

    * **Minimum Count:** This is the minimum count that a gene must have. It can be combined with either Filter
      on Total Count or Minimum Samples.

    * **Filter on Total Count:** This can be used with the Minimum Count filter to keep genes
      with a minimum total read count.

    * **Minimum Samples:** This is the number of samples in which the Minimum CPM/Count
      requirement must be met in order for that gene to be kept.

If the Minimum Samples filter is applied, only genes that exhibit a CPM/count greater than the required amount in at least the number of samples specified will be used for analysis. Care should be taken to
ensure that the sample requirement is appropriate. In the case of an experiment
with two experimental groups each with two members, if there is a change from
insignificant CPM/count to significant CPM/count but the sample requirement is set to 3,
then this will cause that gene to fail the criteria. When in doubt simply do not
filter or consult the `edgeR workflow article`_ for filtering recommendations.

**Advanced Options:**

By default error rate for multiple testing is controlled using Benjamini and
Hochberg's false discovery rate control at a threshold value of 0.05. However
there are options to change this to custom values.

    * **Minimum log2-fold-change Required:**
      In addition to meeting the requirement for the adjusted statistic for
      multiple testing, the observation must have an absolute log2-fold-change
      greater than this threshold to be considered significant, thus highlighted
      in the MD plot.

    * **Adjusted Threshold:**
      Set the threshold for the resulting value of the multiple testing control
      method. Only observations whose statistic falls below this value is
      considered significant, thus highlighted in the MD plot.

    * **P-Value Adjustment Method:**
      Change the multiple testing control method, the options are BH(1995) and
      BY(2001) which are both false discovery rate controls. There is also
      Holm(1979) which is a method for family-wise error rate control.

**Normalisation Method:**
The most obvious technical factor that affects the read counts, other than gene expression
levels, is the sequencing depth of each RNA sample. edgeR adjusts any differential expression
analysis for varying sequencing depths as represented by differing library sizes. This is
part of the basic modeling procedure and flows automatically into fold-change or p-value
calculations. It is always present, and doesn’t require any user intervention.
The second most important technical influence on differential expression is one that is less
obvious. RNA-seq provides a measure of the relative abundance of each gene in each RNA
sample, but does not provide any measure of the total RNA output on a per-cell basis.
This commonly becomes important when a small number of genes are very highly expressed
in one sample, but not in another. The highly expressed genes can consume a substantial
proportion of the total library size, causing the remaining genes to be under-sampled in that
sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely
appear to be down-regulated in that sample . The edgeR `calcNormFactors` function normalizes for RNA composition by finding a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the *effective library size*. The effective library size replaces the original library size in all downsteam analyses. TMM is the recommended method for most RNA-Seq data where the majority (more than half) of the genes are believed not differentially expressed between any pair of the samples. You can change the normalisation method under **Advanced Options** above. For more information, see the `calcNormFactors` section in the `edgeR User's Guide`_.

**Robust Settings**
Option to use robust settings. Using robust settings (robust=TRUE) with the edgeR estimateDisp and glmQLFit functions is usually recommended to protect against outlier genes. This is turned on by default. Note that it is only used with the quasi-likelihood F test method. For more information, see the `edgeR workflow article`_.

**Test Method**
Option to use the likelihood ratio test instead of the quasi-likelihood F test. For more information, see the `edgeR workflow article`_.

.. _edgeR User's Guide: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html

-----

**Outputs**

This tool outputs

    * a table of differentially expressed genes for each contrast of interest
    * a HTML report with plots and additional information

Optionally, under **Output Options** you can choose to output

    * a normalised counts table
    * an RData file

-----

**Citations**

Please try to cite the appropriate articles when you publish results obtained using software, as such citation is the main means by which the authors receive credit for their work. For the edgeR method itself, please cite Robinson et al., 2010, and for this tool (which was developed from the Galaxy limma-voom tool) please cite Liu et al., 2015.

    ]]></help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btp616</citation>
        <citation type="doi">10.1093/nar/gkv412</citation>
    </citations>
</tool>
author	iuc
date	Tue, 30 Jan 2018 04:07:08 -0500
parents	9bdff28ae1b1
children	a1634a9c2ee1