1. ARCH/VCDE F2F BoF And the Presentation Subtitle Goes Here Ravi Madduri December 2008

2. Interview with Jim McCusker Q. Find differential gene expression analysis with Grid Services. Use phenotypic data in caTissue (data service). Use caArray (data service) to get gene expression data. Use the Comparative Marker Selection module in GenePattern (analytical service) to determine differential expression of genes. caTissue --  caArray -  GenePattern Potential data sets were identified

3. Interview with Jim McCusker Two related scenarios You collect samples of normal and a tumor type using caTissue. Run a gene expression experiment and store in caArray. Use GenePattern to do the analysis. caTissue --  caArray -  GenePattern You read a paper and you want to repeat and extend the results and look at different parameters for differential expression. You retrieve the expression data in caArray, do the analysis in GenePattern and with the resulting list, want to find the sample annotations in caArray and possibly retrieve more or related biomaterials. caArray--  GenePattern--  caTissue

4. Interview with Lewis Frey He has a microarray normalization algorithm that he would like to compare against other normalization algorithms. Create a module for an analytical service to access microarray data sets in caArray for analysis. With the existing workflow you can create other services to do an analysis Compare the analyses and validate the algorithm Algorithm and caArray data sets are available

5. Interview with Sakish Patel Raw microarray data and convert to format for R analysis/normalization of the data. Use algorithms to determine differential expression Run through clustering application Run algorithms to automate determine strongest signal Do Pathway analysis of differentially expressed genes Other post processing with ontologic relationships caArray  GenePattern or geWorkbench (have the agorithms)

6. Interview with Mark Adams Looking for biomarkers for treatment efficacy. I’d like to analyze a cohort derived from a translational data set from which I can carry out gene profiling expression analysis and where gene regulation is tied to clinical outcomes. breast cancer data set in I-SPY looked for response to anthracycline Using the scores around tumor size to determine the list of patients that responded and determine the list of those that did not Use the two lists of patient ids to query the caArray data set from each Analyze data in geWorkbench or GenePattern and determine clusters Map clusters to Protein Interaction Database Determine where genetic variability affects a pathway Look at gene SNPS from this list and find which are associated with onset of breast cancer using CGEMS. Query I-SPY  caArray  GenePattern or geWorkbench  CGEMS