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

Alain B Tchagang1*, Sieu Phan1, Fazel Famili1, Heather Shearer2, Pierre Fobert2, Yi Huang23, Jitao Zou2, Daiqing Huang2, Adrian Cutler2, Ziying Liu1 and Youlian Pan1

Author Affiliations

1 Knowledge Discovery Group, Institute for Information Technology, National Research Council Canada, 1200 Montréal Road, Ottawa, ON K1A 0R6, Canada

2 Seed Systems Group, Plant Biotechnology Institute, 110 Gymnasium Place, Saskatoon, SK S7N 0W9, Canada

3 Oil Crops Research, Institute, Chinese Academy of Agricultural Sciences, No. 2 Xudong 2nd Road, Wuhan, Hubei 430062, China

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BMC Bioinformatics 2012, 13:54  doi:10.1186/1471-2105-13-54

Published: 4 April 2012



Nowadays, 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.


We 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.


Our 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.