Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

Abstract

BackgroundNowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.ResultsWe developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.ConclusionsOur analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

Bioinformatics identification of new targets for improving low temperature stress tolerance in spring and winter wheat

“…The Order Preserving Triclustering Algorithm (OPTricluster) [27] was used to model the three dimensional features in the dataset. For example, in the dataset GSE23889, the three dimensions are G × S × T, where G  =  {g 1 , g 2 , …, g n } is the set of n probe sets on the microarray, S = {wM , wN , sM , sN} the set of l (4) wheat genotypes and T = {0, 2, 14, 21, 35, 42, 56, 70} the m (8) time points.…”

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

“…We define a coherent tricluster as a set of genes which exhibits either similar numeric values for the times and conditions (coherent values [21]) or similar behaviors regardless of the exact numeric values: correlated positive and negative changes in the expression values (coherent behavior [21]). Both types of coherent clusters may contain information useful to identify useful phenotypes, potential genes related to these phenotypes and their regulation relations [36].…”

TriClust: A Tool for Cross‐Species Analysis of Gene Regulation

“…While biclustering is defined over two dimensions, there are some algorithms developed for three‐dimensional time‐series gene expression data, which mine clusters along the gene‐sample‐time or gene‐sample‐region dimensions 13,14. Zhao and Zaki13 proposed an algorithm named TriCluster for mining clusters in three‐dimensional microarray data, which is the first three‐way clustering algorithm defined over gene‐sample‐time microarray data.…”

“…Zhao and Zaki13 proposed an algorithm named TriCluster for mining clusters in three‐dimensional microarray data, which is the first three‐way clustering algorithm defined over gene‐sample‐time microarray data. In another study, Tchagang et al 14. developed a subspace clustering algorithm named Order Preserving Triclustering for 3D time series data.…”