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Correlating measurements across samples improves accuracy of large-scale expression profile experiments

Mariano Javier Alvarez1, Pavel Sumazin1, Presha Rajbhandari1 and Andrea Califano12*

Author Affiliations

1 Joint Centers for Systems Biology, Columbia University, 2960 Broadway, New York, NY 10027-6900, USA

2 Department of Biomedical Informatics and Institute for Cancer Genetics and Herbert Irving Comprehensive Cancer Center, Columbia University, 2960 Broadway, New York, NY 10027-6900, USA

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Genome Biology 2009, 10:R143  doi:10.1186/gb-2009-10-12-r143

Published: 30 December 2009

Abstract

Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies.