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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/limma_voom commit 1640914b9812b0482a3cf684f05465f8d9cfdc65
| author | iuc |
|---|---|
| date | Wed, 31 Jan 2018 12:45:42 -0500 |
| parents | a330ddf43861 |
| children | a61a6e62e91f |
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<tool id="limma_voom" name="limma" version="3.34.6.0"> <description> Perform differential expression with limma-voom or limma-trend </description> <requirements> <requirement type="package" version="3.34.6">bioconductor-limma</requirement> <requirement type="package" version="3.20.7">bioconductor-edger</requirement> <requirement type="package" version="1.4.30">r-statmod</requirement> <requirement type="package" version="0.5.0">r-scales</requirement> <requirement type="package" version="0.2.15">r-rjson</requirement> <requirement type="package" version="1.20.0">r-getopt</requirement> </requirements> <version_command><![CDATA[ echo $(R --version | grep version | grep -v GNU)", limma version" $(R --vanilla --slave -e "library(limma); cat(sessionInfo()\$otherPkgs\$limma\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", edgeR version" $(R --vanilla --slave -e "library(edgeR); cat(sessionInfo()\$otherPkgs\$edgeR\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", statmod version" $(R --vanilla --slave -e "library(statmod); cat(sessionInfo()\$otherPkgs\$statmod\$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__/limma_voom.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' #if $deMethod.de_select == 'voom': #if $deMethod.weightOption: -w #end if #elif $deMethod.de_select == 'trend': -t $deMethod.prior_count #end if -n '$adv.normalisationOption' #if $adv.robOption: -b #end if && mkdir ./output_dir && cp '$outReport.files_path'/*.tsv output_dir/ ]]></command> <inputs> <!-- DE Method Option --> <conditional name="deMethod"> <param name="de_select" type="select" label="Differential Expression Method" help="Select the limma-voom or limma-trend method. See Help section below for more information. Default: limma-voom"> <option value="voom" selected="True">limma-voom</option> <option value="trend">limma-trend</option> </param> <when value="voom"> <param name="weightOption" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Apply voom with sample quality weights?" help="Apply weights if outliers are present (voomWithQualityWeights). Default: False."> </param> </when> <when value="trend"> <param name="prior_count" type="float" min="0" value="3" label="Prior count" help="Average count to be added to each observation to avoid taking log of zero. Default: 3." /> </when> </conditional> <!-- 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" /> </section> </inputs> <outputs> <data name="outReport" format="html" label="${tool.name} on ${on_string}: Report" /> <collection name="outTables" type="list" label="${tool.name} on ${on_string}: Tables"> <discover_datasets pattern="(?P<name>.+)\.tsv$" 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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_Mut-WT.tsv" /> <element name="limma-voom_WT-Mut" ftype="tabular" file="limma-voom_WT-Mut.tsv" /> </output_collection> <output name="outReport" > <assert_contents> <has_text text="Limma Analysis Output" /> <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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_Mut-WT.tsv" /> <element name="limma-voom_normcounts" ftype="tabular" file="limma-voom_normcounts.tsv" /> </output_collection> </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="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_Mut-WT_2fact_anno.tsv" /> <element name="limma-voom_WT-Mut" ftype="tabular" file="limma-voom_WT-Mut_2fact_anno.tsv" /> <element name="limma-voom_normcounts" ftype="tabular" file="limma-voom_normcounts_anno.tsv" /> </output_collection> </test> <!-- Ensure limma-trend 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="de_select" value="trend" /> <param name="rdaOption" value="true" /> <output name="outReport" > <assert_contents> <has_text text="The limma-trend method was used" /> </assert_contents> </output> <output_collection name="outTables" count="1"> <element name="limma-trend_Mut-WT" ftype="tabular" file="limma-trend_Mut-WT.tsv" /> </output_collection> </test> </tests> <help><![CDATA[ .. class:: infomark **What it does** Given a matrix of counts (e.g. from featureCounts) and optional information about the genes, performs differential expression (DE) using the limma_ Bioconductor package and produces plots and tables useful in DE analysis. In the `limma approach`_ to RNA-seq, read counts are converted to log2-counts-per-million (logCPM) and the mean-variance relationship is modelled either with precision weights or with an empirical Bayes prior trend. The precision weights approach is called “voom” and the prior trend approach is called “limma-trend”. For more information, see the Help section below. ----- **Inputs** **Differential Expression Method:** Option to use the limma-voom or limma-trend approach for differential expression. The default is limma-voom. If the sequencing depth is reasonably consistent across the RNA samples, then the simplest and most robust approach to differential expression is to use limma-trend. This approach will usually work well if the ratio of the largest library size to the smallest is not more than about 3-fold. When the library sizes are quite variable between samples, then the voom approach is theoretically more powerful than limma-trend. For more information see the excellent `limma User's Guide`_. **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 ========== ======= ======= ======= ======== ======== ======== **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 ========== ========== =================================================== **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 `limma User's Guide`_ 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 with eBayes, used by both liamm-voom and limma-trend. Using robust settings is usually recommended to protect against outlier genes, for more information see the `limma User's Guide`_. This is turned on by default. **Prior Count:** If the limma-trend method is used, a count (`prior.count`) is added to all counts to avoid taking a log of zero, and damp down the variances of logarithms of low counts. A default of 3 is used, as recommended in the `limma User's Guide`_. **Apply Sample Weights:** If the limma-voom method is used, an option is available to downweight outlier samples, such that their information is still used in the statistical analysis but their impact is reduced. Use this whenever significant outliers are present. The MDS plotting tool in this package is useful for identifying outliers. For more information on this option see Liu et al. (2015). **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. limma Please cite the paper below for the limma software itself. Please also try to cite the appropriate methodology articles that describe the statistical methods implemented in limma, depending on which limma functions you are using. The methodology articles are listed in Section 2.1 of the `limma User's Guide`_. * Smyth GK (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397-420. * Law CW, Chen Y, Shi W, and Smyth GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. * Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME, Asselin-Labat ML, Smyth GK, Ritchie ME (2015). Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Research, 43(15), e97. * Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A., and Smyth, G. K. (2006). Empirical array quality weights for microarray data. BMC Bioinformatics 7, Article 261. edgeR Please cite the first paper for the software itself and the other papers for the various original statistical methods implemented in edgeR. See Section 1.2 in the `edgeR User's Guide`_ for more detail. * Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 * Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887 * Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332 * McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297 Please report problems or suggestions to: su.s@wehi.edu.au .. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html .. _limma approach: https://www.ncbi.nlm.nih.gov/pubmed/25605792 .. _limma User's Guide: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html .. _edgeR User's Guide: https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf ]]></help> <citations> <citation type="doi">10.1186/gb-2014-15-2-r29</citation> <citation type="doi">10.1093/nar/gkv412</citation> </citations> </tool>