Identification of differential genes using the LIMMA Bioconductor package within R

Created: 2008-07-02 11:24:12 Last updated: 2008-07-02 11:28:21

Download Workflow

This workflow starts by retrieving the names of microarray datasets from the Maxd database. The user has to select sets of control and test data which are then analysed by the LIMMA Bioconductor package in an R script. This produces a list of significant differentially expressed genes which is then analysed using the Go Term Finder tool to generate a PDF report of the common GO terms associated with the genes. A CSV file containing the list of significant genes is also generated.

Preview

Download as scalable diagram (SVG)

Run

Run this Workflow in the Taverna Workbench...

Option 1:

Copy and paste this link into File > 'Open workflow location...'
http://www.myexperiment.org/workflows/246/download?version=1
[ More Info Expand ]

Workflow Components

Inputs (0)

Processors (19)

Name	Type	Description
flatten	local	Removes a layer of lists
GoTermFinder_pvalue	stringconstant
performT_testsUsingLIMMA	rshell	This R script performs a series of t-tests for each gene expression level between its 2 input data sets using the LIMMA package from Bioconductor.
getNamesUsingXPath	local	Parses the measurement names of microarray datasets from the XML output from maxdBrowse
createFinalCSV	beanshell	Creates a text file in CSV format contain a list of the all the differentially expressed genes identified by the R script.
cleanCSV	beanshell	Removes unneccesary prefix text in the affy identifiers from the CSV file output generated by the R script
createGeneList	beanshell	Create a list of genes for analysis by the GOTermFinder service. Spurious genes are removed including those which have been annotated as questioable and hypothetical and therefore will not have a GO term asscoiated with them.
selectTestData	beanshell	Uses Java Swing classes to enable the user to select data sets for t-test analysis
getAffyIds	beanshell	Parses the affy identifiers from the CSV file
mergeOutput2	beanshell	Combines the selected data sets into a single data set
selectControlData	beanshell	Uses Java Swing classes to enable the user to select data sets for t-test analysis
mergeOutput1	beanshell	Merges the selected input data sets into a single data set
queryMaxd2	arbitrarywsdl	Will take a list of Gene names, a list of Measurement names and a list of Column Types and produce a table. All values are coloured in relation to the a reference. In the list of Measurement names, the first Measurement listed is taken to be the reference. Formatted as text.
queryMaxd1	arbitrarywsdl	Will take a list of Gene names, a list of Measurement names and a list of Column Types and produce a table. All values are coloured in relation to the a reference. In the list of Measurement names, the first Measurement listed is taken to be the reference. Formatted as text.
getGeneName	arbitrarywsdl	Gets the gene name for a given yeast open reading frame number
analyseGenesPDFOutput	arbitrarywsdl	Analyses a list of genenames using the GOTermFinder service
getMeasurementNames	arbitrarywsdl	Querys the maxdload database using maxdBrowse to obtain a list of measurement names from the big experiment data stored there
getTargetDescription	arbitrarywsdl	Gets a description of a given affy identifer using the YeastAffymapper service
getTranscriptId	arbitrarywsdl	Gets the open reading frame number for a given affy identifier

Beanshells (8)

Name	Description	Inputs	Outputs
createFinalCSV	Creates a text file in CSV format contain a list of the all the differentially expressed genes identified by the R script.	affyIds orfs genenames descriptions	csv
cleanCSV	Removes unneccesary prefix text in the affy identifiers from the CSV file output generated by the R script	in	out
createGeneList	Create a list of genes for analysis by the GOTermFinder service. Spurious genes are removed including those which have been annotated as questioable and hypothetical and therefore will not have a GO term asscoiated with them.	listOfOrfs listOfGenenames listOfDescriptions	orfs spurious_genes
selectTestData	Uses Java Swing classes to enable the user to select data sets for t-test analysis	input	output
getAffyIds	Parses the affy identifiers from the CSV file	inList	outList
mergeOutput2	Combines the selected data sets into a single data set	in	out
selectControlData	Uses Java Swing classes to enable the user to select data sets for t-test analysis	input	output
mergeOutput1	Merges the selected input data sets into a single data set	in	out

Outputs (3)

Name	Description
pdf	A PDF document generated by the GoTermFinder service
csv	A text file in CSV format showing a list of differentially expressed genes and their annotations
spurious_genes	A text file in CSV format shoing a list of genes annotated as hypothetical, questionable, genomic sequences, etc. These genes do not have a GO term associated with them and so are removed from the list sent to the GoTermFinder service.

Links (27)

Source	Sink
GoTermFinder_pvalue:value	analyseGenesPDFOutput:pvalue
cleanCSV:out	flatten:inputlist
createGeneList:orfs	analyseGenesPDFOutput:genes
flatten:outputlist	createFinalCSV:affyIds
flatten:outputlist	getAffyIds:inList
getAffyIds:outList	getGeneName:affyId
getAffyIds:outList	getTargetDescription:affyId
getAffyIds:outList	getTranscriptId:affyId
getGeneName:getGeneSymbolReturn	createFinalCSV:genenames
getGeneName:getGeneSymbolReturn	createGeneList:listOfGenenames
getMeasurementNames:return	getNamesUsingXPath:xml-text
analyseGenesPDFOutput:analyseGenesPDFOutputReturn	pdf
createFinalCSV:csv	csv
createGeneList:spurious_genes	spurious_genes
getNamesUsingXPath:nodelist	selectControlData:input
getNamesUsingXPath:nodelist	selectTestData:input
getTargetDescription:getTargetDescriptionReturn	createFinalCSV:descriptions
getTargetDescription:getTargetDescriptionReturn	createGeneList:listOfDescriptions
getTranscriptId:getTranscriptIdReturn	createFinalCSV:orfs
getTranscriptId:getTranscriptIdReturn	createGeneList:listOfOrfs
mergeOutput1:out	performT_testsUsingLIMMA:control_csv
mergeOutput2:out	performT_testsUsingLIMMA:test_csv
performT_testsUsingLIMMA:file	cleanCSV:in
queryMaxd1:return	mergeOutput1:in
queryMaxd2:return	mergeOutput2:in
selectControlData:output	queryMaxd1:MeasurementNames
selectTestData:output	queryMaxd2:MeasurementNames

Coordinations (1)

Controller	Target
mergeOutput1	selectTestData

Information Workflow Type

Taverna 1

Information Uploader

Peter Li

Information License

All versions of this Workflow are licensed under:

Information Version 1 (of 1)

Information Credits (1)

(People/Groups)

Peter Li

Information Attributions (0)

(Workflows/Files)

None

Information Tags (6)

Uploader tags

Log in to add Tags

Information Shared with Groups (0)

None

Information Featured In Packs (3)

Log in to add to one of your Packs

Information Attributed By (0)

(Workflows/Files)

None

Information Favourited By (1)

AbuJarour

Information Statistics

4680 viewings

3008 downloads

[ see breakdown ]

Citations (0)

None

Version History

In chronological order:

Identification of differential genes using the LIMMA Bioconductor package within R

Created by Peter Li on Wednesday 02 July 2008 11:24:12 (UTC)

Last edited by Peter Li on Wednesday 02 July 2008 11:28:21 (UTC)

Reviews (0)

No reviews yet

Be the first to review!

Comments (0)

View Timeline

No comments yet

Log in to make a comment

Other workflows that use similar services (3)

Only the first 2 workflows that use similar services are shown. View all workflows that use these services.

Taverna 1

Uploader

Peter Li

Identification of differential genes using... (2)

Download

This workflow starts by retrieving the names of microarray datasets from the Maxd database. The user has to select sets of control and test data for analysis using t-tests by R. A list of significant differentially expressed genes is then analysed using the Go Term Finder tool which generates a list of GO terms associated with the genes. A CSV file containing the list of significant genes is also generated.

Created: 2008-04-15 | Last updated: 2008-07-01

Credits: Peter Li

Taverna 1

Uploader

Peter Li

Mapping microarray data onto metabolic pat... (1)

Download

This workflow maps microarray data onto metabolic pathway diagrams represented as SBML models drawn using Cell Designer. To run this workflow requires libsbml to be installed into taverna - see http://www.mcisb.org/software/taverna/libsbml/index.html

Created: 2007-11-14 | Last updated: 2007-11-22

Credits: Peter Li