Mercurial > repos > iuc > limma_voom
diff limma_voom.xml @ 0:bdebdea5f6a7 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/limma_voom commit 2f34a215c35f08c3666f314a87d235437baa1d21
| author | iuc |
|---|---|
| date | Mon, 12 Jun 2017 07:41:02 -0400 |
| parents | |
| children | 76d01fe0ec36 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/limma_voom.xml Mon Jun 12 07:41:02 2017 -0400 @@ -0,0 +1,388 @@ +<tool id="limma_voom" name="limma-voom" version="1.1.1"> + <description> + Differential expression with optional sample weights + </description> + + <requirements> + <requirement type="package" version="3.16.5">bioconductor-edger</requirement> + <requirement type="package" version="3.30.13">bioconductor-limma</requirement> + <requirement type="package" version="1.4.29">r-statmod</requirement> + <requirement type="package" version="0.4.1">r-scales</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" $(R --vanilla --slave -e "library(edgeR); cat(sessionInfo()\$otherPkgs\$edgeR\$Version)" 2> /dev/null | grep -v -i "WARNING: ") + ]]> + </version_command> + + <command detect_errors="exit_code"> + <![CDATA[ + Rscript '$__tool_directory__/limma_voom.R' + '$counts' + + #if $anno.annoOpt=='yes': + '$geneanno' + #else: + None + #end if + + '$outReport' + '$outReport.files_path' + $rdaOption + $normalisationOption + $weightOption + '$contrast' + + #if $filterCPM.filterLowCPM=='yes': + '$filterCPM.cpmReq' + '$filterCPM.sampleReq' + #else: + 0 + 0 + #end if + + #if $testOpt.wantOpt=='yes': + '$testOpt.pAdjust' + '$testOpt.pVal' + '$testOpt.lfc' + #else: + "BH" + 0.05 + 0 + #end if + + '$factName::$factLevel' + + && + mkdir ./output_dir + + && + mv '$outReport.files_path'/*.tsv output_dir/ + + ]]> + </command> + + <inputs> + <param name="counts" type="data" format="tabular" label="Counts Data"/> + + <conditional name="anno"> + <param name="annoOpt" type="select" + label="Use Gene Annotations?" + help="If an annotation file is provided, annotations will be added to the table of differential expression results to provide descriptions for each gene."> + <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> + + <!--*Code commented until solution for multiple factors is found* + <repeat name="factors" title="Factors" min="1" max="5" default="1"> + <param name="factName" type="text" label="Factor Name (No spaces)" + help="Eg. Genotype"/> + <param name="factLevel" type="text" size="100" + label="Factor Levels (No spaces)" + help="Eg. WT,WT,Mut,Mut,WT"/> + </repeat> + --> + + <param name="factName" type="text" label="Factor Name" help="Eg. Genotype."/> + <param name="factLevel" type="text" label="Factor Values" + help="Eg. WT,WT,WT,Mut,Mut,Mut + NOTE: Please ensure that the same levels are typed identically with cases matching."/> + <param name="contrast" type="text" label="Contrasts of interest" help="Eg. Mut-WT,KD-Control"/> + + <conditional name="filterCPM"> + <param name="filterLowCPM" type="select" label="Filter Low CPM?" + help="Treat genes with very low expression as unexpressed and filter out to speed up computation."> + <option value="yes" selected="True">Yes</option> + <option value="no">No</option> + </param> + <when value="yes"> + <param name="cpmReq" type="float" value="0.5" min="0" label="Minimum CPM"/> + + <param name="sampleReq" type="integer" value="1" min="0" label="Minimum Samples" + help="Filter out all the genes that do not meet the minimum CPM in at least this many samples."/> + </when> + <when value="no"/> + </conditional> + + <param name="weightOption" type="boolean" truevalue="yes" falsevalue="no" checked="false" label="Apply sample weights?" + help="Apply weights if outliers are present."> + </param> + + <param name="normalisationOption" type="select" label="Normalisation Method"> + <option value="TMM">TMM</option> + <option value="RLE">RLE</option> + <option value="upperquartile">Upperquartile</option> + <option value="none">None (Don't normalise)</option> + </param> + + <param name="rdaOption" type="boolean" truevalue="yes" falsevalue="no" checked="false" + label="Output RData?" + 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."> + </param> + + <conditional name="testOpt"> + <param name="wantOpt" type="select" label="Use Advanced Testing Options?" + help="Enable choices for p-value adjustment method, p-value threshold and log2-fold-change threshold."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <when value="yes"> + <param name="pAdjust" type="select" label="P-Value Adjustment Method."> + <option value="BH">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="pVal" type="float" value="0.05" min="0" max="1" + label="Adjusted Threshold" + help="Genes below this threshold are considered significant and highlighted in the MA plot. If either BH(1995) or BY(2001) were selected then this value is a false-discovery-rate control. If Holm(1979) was selected then this is an adjusted p-value for family-wise error rate."/> + <param name="lfc" type="float" value="0" min="0" + label="Minimum log2-fold-change Required" + help="Genes above this threshold and below the p-value threshold are considered significant and highlighted in the MA plot."/> + </when> + <when value="no"/> + </conditional> + + </inputs> + + <outputs> + <data format="html" name="outReport" label="${tool.name} on ${on_string}: Report" /> + <collection name="voom_results" type="list" label="${tool.name} on ${on_string}: DE genes"> + <discover_datasets pattern="(?P<name>.+)\.tsv$" format="tabular" directory="output_dir" visible="false" /> + </collection> + </outputs> + + <tests> + <test> + <param name="counts" value="matrix.txt" /> + <param name="factName" value="Genotype" /> + <param name="factLevel" value="WT,WT,WT,Mut,Mut,Mut" /> + <param name="contrast" value="Mut-WT,WT-Mut" /> + <param name="normalisationOption" value="TMM" /> + <output_collection name="voom_results" 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-voom Analysis Output" /> + <not_has_text text="RData" /> + </assert_contents> + </output> + </test> + <test> + <param name="annoOpt" value="yes" /> + <param name="geneanno" value="anno.txt" /> + <param name="counts" value="matrix.txt" /> + <param name="factName" value="Genotype" /> + <param name="factLevel" value="WT,WT,WT,Mut,Mut,Mut" /> + <param name="contrast" value="Mut-WT" /> + <param name="normalisationOption" value="TMM" /> + <output_collection name="voom_results" > + <element name="limma-voom_Mut-WT" ftype="tabular" file="limma-voom_Mut-WTanno.tsv" /> + </output_collection> + </test> + <test> + <param name="rdaOption" value="yes" /> + <param name="counts" value="matrix.txt" /> + <param name="factName" value="Genotype" /> + <param name="factLevel" value="WT,WT,WT,Mut,Mut,Mut" /> + <param name="contrast" value="Mut-WT" /> + <param name="normalisationOption" value="TMM" /> + <output name="outReport" > + <assert_contents> + <has_text text="RData" /> + </assert_contents> + </output> + </test> + </tests> + + <help> +<![CDATA[ +.. class:: infomark + +**What it does** + +Given a matrix of counts (e.g. from featureCounts) and optional information about the genes, this tool +produces plots and tables useful in the analysis of differential gene +expression. + +----- + +**Inputs** + +**Counts Data:** +A matrix of counts, with rows corresponding to genes +and columns corresponding to 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. + +Example: + + ========== ======= ======= ======= ======== ======== ======== + **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 avaiable in the differential expression results 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 Name:** +The name of the factor being investigated. This tool currently assumes +that only one factor is of interest. + +**Factor Levels:** +The levels of the factor of interest, this must be entered in the same +order as the samples to which the levels correspond as listed in the +columns of the counts matrix. + +The values should be seperated by commas, and spaces must not be used. + +**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. + +The values should be seperated by commas and spaces must not be used. + +**Filter Low CPM:** +Option to ignore the genes that do not show significant levels of +expression, this filtering is dependent on two criteria: + + * **Minimum CPM:** This is the counts per million that a gene must have in at + least some specified number of samples. + + * **Minumum Samples:** This is the number of samples in which the CPM + requirement must be met in order for that gene to be acknowledged. + +Only genes that exhibit a CPM 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 to significant cpm 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. + + +**Normalisation Method:** +Option for using different methods to rescale the raw library +size. For more information, see calcNormFactor section in the edgeR_ user's +manual. + +**Apply Sample Weights:** +Option 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). + +**Use Advanced Testing 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. + + * **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. + + * **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 MA plot. + + * **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 MA plot. + +----- + +**Citations:** + +.. class:: infomark + +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. + +.. class:: infomark + +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 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 + +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html +]]> + </help> + <citations> + <citation type="doi">10.1093/nar/gkv412</citation> + </citations> +</tool>