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Tmm: Analysis of Multiple Microarray Data Sets Richard Moffitt Georgia Institute of Technology 29 June, 2006 Goal • Use 60 large human microarray datasets. (3924 arrays) • Find reliably coexpressed genes. • http://benzer.ubic.ca/cgi-bin/find-links.cgi – (just google ‘tmm microarray’) Usage Case • Query by gene or probe ID. • Set stringency level. How it Works • Looks for genes that coexpress with the queried-for gene. – correlates gene expression profiles • Stringency requirement eliminates weak links. Our Query • RAP1GSD1, a biomarker form Chang et al • 2 minutes later… Our Results • List of linked genes and some statistics. Visualization • Visualizations of coexpresed gene profiles for each dataset used. Query #2 LETMD1, a biomarker from Citation Spira A, Am J Respir Cell Mol Biol. 2004 Phenotypes_Being_Studied No or mild emphysema, severe emphysema Chip_Platform GPL96: Affymetrix GeneChip Human Genome U133 Array Set HG-U133A for 712X712 Results #2 : Why? • Our first query was from one of the datasets used by Tmm. Synonym Search Conclusion • Useful to make a small list of probable targets. • Useful for some validation? – Similar to GOMiner validation. – Speed will inhibit this. • Semantics is a barrier to usefulness. Acknowledgements • Thanks to: Deepak Sambhara JT Torrance Lauren Smalls-Mantey Malcolm Thomas Randy Han and Kiet Hyun for curating all the biomarker data that was used for test queries.