Unravelling the Yeast Cell Cycle Using the TriGen Algorithm

Abstract

Abstract. Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we present the results of applying the TriGen algorithm, a genetic algorithm that finds triclusters that take into account the experimental conditions and the time points, to the yeast cell cycle problem, where the goal is to identify all genes whose expression levels are regulated by the cell cycle.

Revisiting the yeast cell cycle problem with the improved TriGen algorithm

“…Regarding the results obtained in [9], we can see that the changes on the TriGen algorithm have improved the quality of the solutions in terms of pattern finding and solution distribution over the input data. Generally, we see that the algorithm has been capable to group together genes with very similar gene expression values for the three dimensions visited.…”

“…We have applied the TriGen algorithm to the yeast (Saccharomyces Cerevisiae) cell cycle problem [15] as we did in [9]. The yeast cell cycle analysis project's goal is to identify all genes whose mRNA levels are regulated by the cell cycle.…”

“…Samples were taken at 2 time points for cln3, 2 for clb2, 18 for pheromone, 24 for cdc15, 17 for cdc28 and 14 for elutriation. As we did in [9], to apply the TriGen algorithm we did not take into account the conditions with only 2 time points, since they are not relevant for a time course experiment, so we used the first 14th time points of the pheromone, cdc15, cdc28 and elutriation experiment. Therefore our dataset contains 14 time points, 6179 genes and 4 conditions.…”

“…Therefore, the use of specific tools for data analysis in which genes are evaluated under certain conditions considering the time factor becomes necessary. The TriGen algorithm, presented in [9], goes a step further than clustering and biclustering techniques in the creation of groups of pattern similarity for genes. TriGen works on a three-dimensional space, thus taking into account the time factor, and allowing the evaluation of the behavior of genes only under certain conditions and only under certain time points.…”

“…In this article we present some changes made to the TriGen algorithm, in particular to the initial population creation method and the organization of the input data to avoid overlapping on the solutions. The algorithm is applied to the yeast (Saccharomyces Cerevisiae) cell cycle problem and the results are compared to the ones obtained in [9]. Other works related with this approach are in [10] and [11].…”