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Richard Moffitt Georgia Institute of Technology 29 June, 2006. Tmm: Analysis of Multiple Microarray Data Sets. 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’). - PowerPoint PPT Presentation
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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
CitationSpira A, Am J Respir Cell Mol Biol. 2004
Phenotypes_Being_StudiedNo or mild emphysema, severe emphysema
Chip_PlatformGPL96: 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.