Improved biclustering on expression data through overlapping control

Abstract

PurposeThe purpose of this paper is to present a novel control mechanism for avoiding overlapping among biclusters in expression data.Design/methodology/approachBiclustering is a technique used in analysis of microarray data. One of the most popular biclustering algorithms is introduced by Cheng and Church (2000) (Ch&Ch). Even if this heuristic is successful at finding interesting biclusters, it presents several drawbacks. The main shortcoming is that it introduces random values in the expression matrix to control the overlapping. The overlapping control method presented in this paper is based on a matrix of weights, that is used to estimate the overlapping of a bicluster with already found ones. In this way, the algorithm is always working on real data and so the biclusters it discovers contain only original data.FindingsThe paper shows that the original algorithm wrongly estimates the quality of the biclusters after some iterations, due to random values that it introduces. The empirical results show that the proposed approach is effective in order to improve the heuristic. It is also important to highlight that many interesting biclusters found by using our approach would have not been obtained using the original algorithm.Originality/valueThe original algorithm proposed by Ch&Ch is one of the most successful algorithms for discovering biclusters in microarray data. However, it presents some limitations, the most relevant being the substitution phase adopted in order to avoid overlapping among biclusters. The modified version of the algorithm proposed in this paper improves the original one, as proven in the experimentation.

Unravelling the Yeast Cell Cycle Using the TriGen Algorithm

“…For this particular work, we have simulated data from 5 different time points and 10 conditions using microarrays containing 1000 genes. Each gene is assigned a random value which is contained in the rank, respectively for each condition, [1,15], [7,35] [10,30]. In such data set, we have allocated a tricluster with all its values fixed to 1.…”

“…Traditional clustering algorithms work on the whole space of data dimensions examining each gene in the dataset under all conditions tested. Biclustering techniques [8] go a step further by relaxing the conditions and by allowing assessment only under a subset of the conditions of the experiment, and it has proved to be successful finding gene patterns [6,10]. However, clustering and biclustering are insufficient when analyzing data from microarray experiments where attention is payed on how the time affects gene's behavior.…”

Revisiting the yeast cell cycle problem with the improved TriGen algorithm

“…For this particular work, we have simulated data from 5 different time points and 10 conditions using microarrays containing 1000 genes. Each gene is assigned a random value which is contained in the rank, respectively for each condition, [1,15], [7,35] [10,30]. In such data set, we have allocated a tricluster with all its values fixed to 1.…”

“…Traditional clustering algorithms work on the whole space of data dimensions examining each gene in the dataset under all conditions tested. Biclustering techniques [5] go a step further by relaxing the conditions and by allowing assessment only under a subset of the conditions of the experiment, and it has proved to be successful finding gene patterns [6], [7]. However, clustering and biclustering …”

Triclustering on temporary microarray data using the TriGen algorithm

“…Traditional clustering algorithms work on the whole space of data dimensions examining each gene in the dataset under all conditions tested. Biclustering techniques [5] go a step further by relaxing the conditions and by allowing assessment only under a subset of the conditions of the experiment, and it has proved to be successful finding gene patterns [6], [7]. However, clustering and biclustering are insufficient when analyzing data from microarray experiments where attention is payed on how the time affects gene's behavior.…”