HUMAN Microarray CEL file to candidate pathways

This workflow takes in a CEL file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the MADAT software package (MicroArray Data Analysis Tool) [http://www.bioinf.manchester.ac.uk/MADAT/index.html]. Also retruned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained. NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory (see for basic info: http:// www. cs. man. ac. uk/ ~fisherp/ rlib.html)

The example inputs for this workflow are as follows: Samples1 = one or more CEL files for cross-correlating with Samples2 CEL files (new line separated including the .CEL): Liver_Day1_Mouse.CEL Liver_Day2_Mouse.CEL Samples2 = one or more CEL files for cross-correlating with Samples1 CEL files (new line separated including the .CEL): Kideny_Day1_Mouse.CEL Kidney_Day2_Mouse.CEL geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20 arrayTypeAffy = the name of the Mouse AffyMetrix array used, e.g. mouse4302, hgu133a... path = the direct path to the CEL file location, e.g. C:/Microarray_Data/CEL_FILES/ - note the forward slashes NormalizationMethod = the type of normalisation to perfrom, e.g. rma, gcrma or mmgmos testMethod = e.g. limma, mmtest or pplr p-value = the p-value cut-off value for the array data, e.g. 0.05 foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1)