From co-expression to co-regulation: how many microarray experiments do we need?

Genome Biology
Ka Yee YeungR E Bumgarner

Abstract

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. 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. Our results are consistent with respect to independent evaluation criteria that strengthen our confidence in our results...Continue Reading

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Citations

Nov 16, 2011·Proceedings of the National Academy of Sciences of the United States of America·Ka Yee YeungAdrian E Raftery
Oct 8, 2005·Omics : a Journal of Integrative Biology·Guanghui HuMark Hurle
Apr 15, 2008·Bioinformatics·Haiyun WangZiyin Shen
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Apr 2, 2021·PloS One·Wencheng YinHumberto Gutierrez

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Methods Mentioned

BETA
immunoprecipitation
environmental stress
ChIP
environmental

Software Mentioned

MCLUST
IMM
KNNimpute

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