Get differentially expressed genes for Array B (one brain region)

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#####################################################################################
###load the data to create the expression set object
#####################################################################################
setwd(datadirectory)

###read.tables
ex_file<-as.matrix(read.table(expression_file, header=TRUE, sep="\t", check.names=FALSE))
pheno_file<-read.table(phenotype_file, header=TRUE, sep="\t", check.names=FALSE)

Name	Description
exprs_file	Location of the gene expression data
phenotypeData	Location of the phenotype data
cf	This input port takes an integer 1 or 2 or 3, which represents the first, second or third coefficient that we want to export from top table (limma), when we test for differential expression. We tested for DE between HD and normal tissue in each brain region. Therefore cf = 1 -> caudate nucleus cf = 2 -> frontal lobe cf = 3 -> cerebellum
working_dir	the desired working directory. This workflow saves the results in .txt files and therefore its useful for the user to provide a directory to save the results in.

Name	Type	Description
get_necessary_files	rshell	Loads the files we defined at the input ports. Gene expression and phenotypic data Script ##################################################################################### ###load the data to create the expression set object ##################################################################################### setwd(datadirectory) ###read.tables ex_file<-as.matrix(read.table(expression_file, header=TRUE, sep="\t", check.names=FALSE)) pheno_file<-read.table(phenotype_file, header=TRUE, sep="\t", check.names=FALSE) R Server localhost:6311
create_expression_set	rshell	create expression set object to be used by limma to test for DE Script ##create expression set object proceset<-new("ExpressionSet", exprs=ex_file) rownames(pheno_file)<-pheno_file$Hybridization.Name #all(rownames(pheno_a_test)==colnames(ex_a)) p_Data <- new("AnnotatedDataFrame", data = pheno_file) proceset <- new("ExpressionSet", exprs = ex_file,phenoData = p_Data) probe_ids<-rownames(exprs(proceset)) write.table(probe_ids, "probe_ids.txt", col.names=FALSE, row.names=FALSE) print("expression set object done") out1<-"blabla" R Server localhost:6311
libraries	rshell	Load necessary libraries. If not existing libraries need to be installed beforehand. these are : library(ArrayExpress) library(hgu133b.db) library(limma) Script library(ArrayExpress) library(hgu133b.db) #library("Biobase") #library("limma") R Server localhost:6311
compute_DE_limma	workflow
probenames_to_entrez_genes_ids_Array_B	rshell	translates probe names to entrez gene ids. For array B. Script ##Get the matching gene ids for the probe ids entrez_ids<-hgu133bENTREZID[probe_ids_new] entrez_ids<-toTable(entrez_ids) print("probe names to entrez_ids done") R Server localhost:6311
get_DE_genes	workflow
cut_off	stringconstant	cut_off value for exporting DE genes. Here it is defined as 0.05. for other p value cut offs this constant needs to be changed Value 0.05

Name	Description
filename_gene_ids
file_DE_genes

Source	Sink
phenotypeData	get_necessary_files:phenotype_file
exprs_file	get_necessary_files:expression_file
working_dir	get_necessary_files:datadirectory
create_expression_set:probe_ids	probenames_to_entrez_genes_ids_Array_B:probe_ids_new
cf	get_DE_genes:cf
cut_off:value	get_DE_genes:cut_off
get_DE_genes:filename	filename_gene_ids
get_DE_genes:file_DE	file_DE_genes

Controller	Target
compute_DE_limma	get_DE_genes
libraries	get_necessary_files
get_necessary_files	create_expression_set
probenames_to_entrez_genes_ids_Array_B	get_DE_genes
create_expression_set	compute_DE_limma
create_expression_set	probenames_to_entrez_genes_ids_Array_B
libraries	create_expression_set

Get differentially expressed genes for Array B (one brain region)

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