Affiliations: Centre of Informatics, Federal University of
Pernambuco, Av. Professor Luis Freire, s/n, 50740-540, Recife, PE, Brazil.
E-mail: {igcf, fatc}@cin.ufpe.br | ICMC, University of São Paulo, Caixa Postal 668,
13560-970, São Carlos, SP, Brazil. E-mail: [email protected]
Note: [] Corresponding author
Abstract: In the computational analysis of gene expression time series, the
main aspect in finding co-expressed genes is the proximity (similarity or
dissimilarity) index used in the clustering method. In this context, the
proximity indices should find genes that have similar patterns of expression
change through time. There are a number of proximity indices used for such a
task. However, the majority of these works has given emphasis on the biological
results, with no critical evaluation of the suitability of the proximity index
used. As a consequence, so far, there is no validity study on which proximity
indices are more suitable for the analysis of gene expression time series.
Based on this, a comparative study of proximity indices broadly used in the
literature is accomplished in this work. More specifically, versions of three
distinct proximity indices are compared: Euclidean distance, Pearson
correlation and angular separation. In order to evaluate the results, an
adaptation of the k-fold cross-validation procedure suitable for
unsupervised methods is used. The accuracies of the proximity indices are
assessed with the use of an external index, which measures the agreement
between the clustering results and gene annotation data.
