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Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.

Liu T, Lin N, Shi N, Zhang B - BMC Bioinformatics (2009)

Bottom Line: Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time.It is also computationally much faster than Wang et al. 3.In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com

ABSTRACT

Background: Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. 1 proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.

Results: We propose a computationally efficient information criterion-based clustering algorithm, called ORICC, that also takes account of the ordering in time-course microarray experiments by embedding the order-restricted inference into a model selection framework. Genes are assigned to the profile which they best match determined by a newly proposed information criterion for order-restricted inference. In addition, we also developed a bootstrap procedure to assess ORICC's clustering reliability for every gene. Simulation studies show that the ORICC method is robust, always gives better clustering accuracy than Peddada's method and saves hundreds of times computational time. Under some scenarios, its accuracy is also better than some other existing clustering methods for short time-course microarray data, such as STEM 2 and Wang et al. 3. It is also computationally much faster than Wang et al. 3.

Conclusion: Our ORICC algorithm, which takes advantage of the temporal ordering in time-course microarray experiments, provides good clustering accuracy and is meanwhile much faster than Peddada's method. Moreover, the clustering reliability for each gene can also be assessed, which is unavailable in Peddada's method. In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

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Breast cancer cell line data: Temporal profiles of clusters from the ORICC analysis. Curves are given by connecting the observed log expression ratios at different time points.
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Figure 14: Breast cancer cell line data: Temporal profiles of clusters from the ORICC analysis. Curves are given by connecting the observed log expression ratios at different time points.

Mentions: We further applied STEM and Wang's method on the breast cancer cell line data. Table 1 reports Rand's C statistics among results from four clustering methods. It shows that the three profile matching algorithms have results more alike each other, while the unsupervised Wang's method is less similar to the rest. This observation can also be seen in the temporal profiles of the clusters given by four different methods (Figures 14, 15, 16 and 17). While ORICC and Peddada's method give ten clusters plus the 'flat' cluster, Wang's method identifies seven clusters, and STEM reports twelve significant clusters.


Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.

Liu T, Lin N, Shi N, Zhang B - BMC Bioinformatics (2009)

Breast cancer cell line data: Temporal profiles of clusters from the ORICC analysis. Curves are given by connecting the observed log expression ratios at different time points.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2696449&req=5

Figure 14: Breast cancer cell line data: Temporal profiles of clusters from the ORICC analysis. Curves are given by connecting the observed log expression ratios at different time points.
Mentions: We further applied STEM and Wang's method on the breast cancer cell line data. Table 1 reports Rand's C statistics among results from four clustering methods. It shows that the three profile matching algorithms have results more alike each other, while the unsupervised Wang's method is less similar to the rest. This observation can also be seen in the temporal profiles of the clusters given by four different methods (Figures 14, 15, 16 and 17). While ORICC and Peddada's method give ten clusters plus the 'flat' cluster, Wang's method identifies seven clusters, and STEM reports twelve significant clusters.

Bottom Line: Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time.It is also computationally much faster than Wang et al. 3.In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com

ABSTRACT

Background: Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. 1 proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.

Results: We propose a computationally efficient information criterion-based clustering algorithm, called ORICC, that also takes account of the ordering in time-course microarray experiments by embedding the order-restricted inference into a model selection framework. Genes are assigned to the profile which they best match determined by a newly proposed information criterion for order-restricted inference. In addition, we also developed a bootstrap procedure to assess ORICC's clustering reliability for every gene. Simulation studies show that the ORICC method is robust, always gives better clustering accuracy than Peddada's method and saves hundreds of times computational time. Under some scenarios, its accuracy is also better than some other existing clustering methods for short time-course microarray data, such as STEM 2 and Wang et al. 3. It is also computationally much faster than Wang et al. 3.

Conclusion: Our ORICC algorithm, which takes advantage of the temporal ordering in time-course microarray experiments, provides good clustering accuracy and is meanwhile much faster than Peddada's method. Moreover, the clustering reliability for each gene can also be assessed, which is unavailable in Peddada's method. In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

Show MeSH
Related in: MedlinePlus