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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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Differences between inner and outer cotyledons. Gene expression differences between inner and outer cotyledons. The x-axis corresponds to the experimental time points and the y-axis to the mean of the expression level of genes in each sample. Each line represents a sample.
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Figure 9: Differences between inner and outer cotyledons. Gene expression differences between inner and outer cotyledons. The x-axis corresponds to the experimental time points and the y-axis to the mean of the expression level of genes in each sample. Each line represents a sample.

Mentions: With the minimum number of samples in a tricluster set to 1 and a threshold of ~ 1.5 fold change, the algorithm generated 22-1 = 3 combinations of samples ({I, O}, {I}, {O}). The subset of sample {I, O} yielded similar patterns between the inner and outer cotyledons, whereas the equations {O} - {I, O}, or {I} - {I, O} yielded patterns specific to outer or inner cotyledon, respectively. Analysis reveals 33 genes depicting the main difference between the two samples across the six time point experiments (Figure 9) and several others across subsets of the six time points (Additional file 4). Among the 33 genes, 17 and 16 were highly expressed in inner compared to outer, and outer compared to inner, respectively.


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)

Differences between inner and outer cotyledons. Gene expression differences between inner and outer cotyledons. The x-axis corresponds to the experimental time points and the y-axis to the mean of the expression level of genes in each sample. Each line represents a sample.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Differences between inner and outer cotyledons. Gene expression differences between inner and outer cotyledons. The x-axis corresponds to the experimental time points and the y-axis to the mean of the expression level of genes in each sample. Each line represents a sample.
Mentions: With the minimum number of samples in a tricluster set to 1 and a threshold of ~ 1.5 fold change, the algorithm generated 22-1 = 3 combinations of samples ({I, O}, {I}, {O}). The subset of sample {I, O} yielded similar patterns between the inner and outer cotyledons, whereas the equations {O} - {I, O}, or {I} - {I, O} yielded patterns specific to outer or inner cotyledon, respectively. Analysis reveals 33 genes depicting the main difference between the two samples across the six time point experiments (Figure 9) and several others across subsets of the six time points (Additional file 4). Among the 33 genes, 17 and 16 were highly expressed in inner compared to outer, and outer compared to inner, respectively.

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