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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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Similarities among samples in mouse data. Similarities in the gene expression data of mice response to Plasmodium chabaudi infection. The x-axis corresponds to the subset of samples and the y-axis the number of genes that behave similarly in this subset of samples.
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Figure 3: Similarities among samples in mouse data. Similarities in the gene expression data of mice response to Plasmodium chabaudi infection. The x-axis corresponds to the subset of samples and the y-axis the number of genes that behave similarly in this subset of samples.

Mentions: Figure 3 shows the set of genes in which the expression level changed similarly by the infection across the time series, in one and a combination of two or more samples. Among the 3943 probes conserved in the four biological samples {IM, IF, GM, GF}, 3516 genes are unchanged, whereas 427 (Figure 3) changed similarly in all four samples. These 427 genes are further clustered into six groups (Figure 4) with 12 or more genes. Clearly, the genes in Figure 4 have similar behaviour in the four samples and across the entire time series. Most of these 427 genes may play the role of housekeeping. In other words, they represent the set of genes that are co-expressed regardless of the experimental condition to maintain basic cellular function. Indeed, Gene Ontology (GO) analysis of these six clusters (Figure 5), showed that they are involved in similar molecular function and biological processes, such as protein and DNA binding, transcription regulation, cell cycle and basic metabolism.


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)

Similarities among samples in mouse data. Similarities in the gene expression data of mice response to Plasmodium chabaudi infection. The x-axis corresponds to the subset of samples and the y-axis the number of genes that behave similarly in this subset of samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Similarities among samples in mouse data. Similarities in the gene expression data of mice response to Plasmodium chabaudi infection. The x-axis corresponds to the subset of samples and the y-axis the number of genes that behave similarly in this subset of samples.
Mentions: Figure 3 shows the set of genes in which the expression level changed similarly by the infection across the time series, in one and a combination of two or more samples. Among the 3943 probes conserved in the four biological samples {IM, IF, GM, GF}, 3516 genes are unchanged, whereas 427 (Figure 3) changed similarly in all four samples. These 427 genes are further clustered into six groups (Figure 4) with 12 or more genes. Clearly, the genes in Figure 4 have similar behaviour in the four samples and across the entire time series. Most of these 427 genes may play the role of housekeeping. In other words, they represent the set of genes that are co-expressed regardless of the experimental condition to maintain basic cellular function. Indeed, Gene Ontology (GO) analysis of these six clusters (Figure 5), showed that they are involved in similar molecular function and biological processes, such as protein and DNA binding, transcription regulation, cell cycle and basic metabolism.

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