Effective similarity measures for expression profiles

Department of Computer Science, Cornell University, NY, USA.
Bioinformatics (Impact Factor: 4.98). 08/2006; 22(13):1616-22. DOI: 10.1093/bioinformatics/btl127
Source: PubMed


It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners; they can participate in the same pathway without interacting directly; they can perform similar functions; or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/

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Available from: Golan Yona,
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    • "As a normalized and dimensionless similarity metric, the correlation coefficient is appropriate for comparing variables with different units or scales, such as different expressive parameters, and is especially useful for comparing profiles or sequences (Hubert, 1979). Thus, correlation coefficients are among the most effective measures for detecting similarity in gene expression profiles (Yona et al., 2006), a research question which has many parallels with the similarity analysis of expressive profiles in music performance . Unlike the parametric Pearson correlation coefficient, non-parametric correlation coefficients such as Spearman's rho and Kendall's tau are not sensitive to outliers and are less affected by the shape of the statistical distribution of the data, making them more widely applicable as similarity indices. "
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    • "For transcriptomic (microarray) and proteomic (mass-spectrometry) data, we use the Pearson correlation coefficient that gives high similarity to genes with correlated transcriptomic/proteomic profiles. Other similarity measures, as the classical Euclidean metric, could be possible, but the Pearson correlation measure has been shown to perform well to detect functional links in several analyses [20]. For the protein-protein interaction data, we use the Czekanovski-Dice metric [21], which gives high similarity to pairs of genes that share many interactors, and has been shown to perform well to predict biological functions [10]. "
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    • "The reason for selecting Pearson correlation is that it has been consistently proven to provide better performance for microarray data compared with other similarity measures [23]. The sub-matrix SM c with the maximum absolute correlation with Y represents the best correlation of Y (Step 4). "

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