1. Inferring Cellular Processes from Coexpressing Genes
Daniel Korenblum
November 26, 2001

2. Motivation for Clustering
High throughput experiments
Reduce complexity by coarse graining:
Extract essential features
Visualize data matrix entries with efficient display
Obtain similarities that reflect biological properties

3. 1998: Eisen, Spellman, Brown, & Botstein
Average Linkage Clustering of Time Courses
Correlation measures similarity (scale invariant)
Fixed offset:
Genes assumed symmetric with respect to changes from reference state
Reorder genes:
Permute rows of expression data matrix
Proximity corresponds to similarity

4. What determines the Patterns
Assess the significance of the clusters
Could results be statistical artifacts?
Swap matrix elements
Apply clustering algorithm:
See different patterns
No prolonged correlations
Signal from different conditions counteracts noise from single observations and cDNA variations
Biologically interpretable implies significant

5. Gene Shaving Avoids a single reordering for all genes
Different genes may require different measures of similarity
Use the principle component of a set of genes (eigengene) as a reference state
Select genes with high covariance with the eigengene

6. Gene Shaving, Cont'd High variation across samples
Strong correlation across genes (coherence)
Hierarchical methods address variations over samples
Supervising affects average gene effects to select strong contributions on predictvie abilities

7. Conclusions Change in methodology over the past few years
Array data holds comprehensive picture of cellular processes