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Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

Priit Adler1, Raivo Kolde23, Meelis Kull23, Aleksandr Tkachenko23, Hedi Peterson13, Jüri Reimand2 and Jaak Vilo23*

  • * Corresponding author: Jaak Vilo vilo@ut.ee

  • † Equal contributors

Author Affiliations

1 Institute of Molecular and Cell Biology, Riia 23, 51010 Tartu, Estonia

2 Institute of Computer Science, University of Tartu, Liivi 2-314, 50409 Tartu, Estonia

3 Quretec, Ülikooli 6a, 51003 Tartu, Estonia

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Genome Biology 2009, 10:R139  doi:10.1186/gb-2009-10-12-r139

Published: 4 December 2009

Abstract

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/ webcite.