From co-expression to co-regulation: how many microarray experiments do we need?
BACKGROUND
Cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance.


RESULTS
We applied various clustering algorithms to microarray datasets with different sizes, and we evaluated the clustering results by determining the fraction of gene pairs from the same clusters that share at least one known common transcription factor. We used both yeast transcription factor databases (SCPD, YPD) and chromatin immunoprecipitation (ChIP) data to evaluate our clustering results. We showed that the ability to identify co-regulated genes from clustering results is strongly dependent on the number of microarray experiments used in cluster analysis and the accuracy of these associations plateaus at between 50 and 100 experiments on yeast data. Moreover, the model-based clustering algorithm MCLUST consistently outperforms more traditional methods in accurately assigning co-regulated genes to the same clusters on standardized data.


CONCLUSIONS
Our results are consistent with respect to independent evaluation criteria that strengthen our confidence in our results. However, when one compares ChIP data to YPD, the false-negative rate is approximately 80% using the recommended p-value of 0.001. In addition, we showed that even with large numbers of experiments, the false-positive rate may exceed the true-positive rate. In particular, even when all experiments are included, the best results produce clusters with only a 28% true-positive rate using known gene transcription factor interactions.
Yeung
Ka Yee
KY
Department of Microbiology, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu
Medvedovic
Mario
M
Bumgarner
Roger E
RE
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends*; D015966:Gene Expression Regulation, Fungal / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends*; D012441:Saccharomyces cerevisiae / Q000235:genetics