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

Tchagang AB, Phan S, Famili F, Shearer H, Fobert P, Huang Y, Zou J, Huang D, Cutler A, Liu Z, Pan Y - BMC Bioinformatics (2012)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Knowledge Discovery Group, Institute for Information Technology, National Research Council Canada, 1200 Montréal Road, Ottawa, ON K1A 0R6, Canada. alain.tchagang@nrc-cnrc.gc.ca

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

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OPTricluster software. Example of OPTricluster interface showing a conserved cluster and its Gene Ontology analysis.
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Figure 1: OPTricluster software. Example of OPTricluster interface showing a conserved cluster and its Gene Ontology analysis.

Mentions: There are three types of patterns that the user can choose from: conserved patterns, divergent patterns, and constant patterns. Conserved patterns correspond to group of genes having same behaviour across experimental time points in subsets of samples. Divergent patterns correspond to group of genes that behave differently in at least one sample along the time point experiments. Constant patterns correspond to groups of genes that the expression levels do not change across experimental time points. Once the type of patterns is selected, a new table appears on the screen, describing the subset of samples the number of genes with the selected patterns in each subset of samples. By clicking on a subset of sample, another table appears showing how the genes are grouped in the selected patterns and in the corresponding subset of samples. Finally the user can view the genes in each group as a table by clicking on the corresponding ranking profile. Figure 1 shows an example of the analysis of a conserved pattern. We refer the reader to the OPTricluster user manual (Additional File 2) for more details relative to OPTricluster Software.


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

Tchagang AB, Phan S, Famili F, Shearer H, Fobert P, Huang Y, Zou J, Huang D, Cutler A, Liu Z, Pan Y - BMC Bioinformatics (2012)

OPTricluster software. Example of OPTricluster interface showing a conserved cluster and its Gene Ontology analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376030&req=5

Figure 1: OPTricluster software. Example of OPTricluster interface showing a conserved cluster and its Gene Ontology analysis.
Mentions: There are three types of patterns that the user can choose from: conserved patterns, divergent patterns, and constant patterns. Conserved patterns correspond to group of genes having same behaviour across experimental time points in subsets of samples. Divergent patterns correspond to group of genes that behave differently in at least one sample along the time point experiments. Constant patterns correspond to groups of genes that the expression levels do not change across experimental time points. Once the type of patterns is selected, a new table appears on the screen, describing the subset of samples the number of genes with the selected patterns in each subset of samples. By clicking on a subset of sample, another table appears showing how the genes are grouped in the selected patterns and in the corresponding subset of samples. Finally the user can view the genes in each group as a table by clicking on the corresponding ranking profile. Figure 1 shows an example of the analysis of a conserved pattern. We refer the reader to the OPTricluster user manual (Additional File 2) for more details relative to OPTricluster Software.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Knowledge Discovery Group, Institute for Information Technology, National Research Council Canada, 1200 Montréal Road, Ottawa, ON K1A 0R6, Canada. alain.tchagang@nrc-cnrc.gc.ca

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus