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Content-based microarray search using differential expression profiles

Jesse M Engreitz1, Alexander A Morgan2, Joel T Dudley23, Rong Chen3, Rahul Thathoo4, Russ B Altman15 and Atul J Butte236*

Author Affiliations

1 Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA

2 Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA

3 Department of Pediatrics and Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

4 Department of Computer Science, Stanford University, Stanford, CA, USA

5 Departments of Genetics and Medicine, Stanford University School of Medicine, Stanford, CA, USA

6 Lucile Packard Children's Hospital, Stanford University, Stanford, CA, USA

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BMC Bioinformatics 2010, 11:603  doi:10.1186/1471-2105-11-603

Published: 21 December 2010



With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.


We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and p-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3.


Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.