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.