Research
Measuring gene expression divergence: the distance to keep
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* Corresponding author: Galina Glazko galina_glazko@urmc.rochester.edu
1 Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA
2 Stowers Institute for Medical Research, 1000 E 50th St., Kansas City MO 64110, USA
3 Department of Microbiology, Molecular Genetics, and Immunology, University of Kansas Medical Center, Kansas City, KS 66160, USA
Biology Direct 2010, 5:51 doi:10.1186/1745-6150-5-51
Published: 6 August 2010Abstract
Background
Gene expression divergence is a phenotypic trait reflecting evolution of gene regulation and characterizing dissimilarity between species and between cells and tissues within the same species. Several distance measures, such as Euclidean and correlation-based distances have been proposed for measuring expression divergence.
Results
We show that different distance measures identify different trends in gene expression patterns. When comparing orthologous genes in eight rat and human tissues, the Euclidean distance identified genes uniformly expressed in all tissues near the expression background as genes with the most conserved expression pattern. In contrast, correlation-based distance and generalized-average distance identified genes with concerted changes among homologous tissues as those most conserved. On the other hand, correlation-based distance, Euclidean distance and generalized-average distance highlight quite well the relatively high similarity of gene expression patterns in homologous tissues between species, compared to non-homologous tissues within species.
Conclusions
Different trends exist in the high-dimensional numeric data, and to highlight a particular trend an appropriate distance measure needs to be chosen. The choice of the distance measure for measuring expression divergence can be dictated by the expression patterns that are of interest in a particular study.
Reviewers
This article was reviewed by Mikhail Gelfand, Eugene Koonin and Subhajyoti De (nominated by Sarah Teichmann).