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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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Simulation 4: Detection error rate. This figure plots the detection error rate of the one-stage ORICC algorithm for a true profile that is not explicitly specified in the candidate profiles.
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Figure 12: Simulation 4: Detection error rate. This figure plots the detection error rate of the one-stage ORICC algorithm for a true profile that is not explicitly specified in the candidate profiles.

Mentions: We generate a data set containing 2000 genes from this profile. At each time point t, we generate M replicates for each gene's expression level from normal distributions with means μt and variance 0.5. Then, we consider candidate profiles being C1, C2, C4 and C9 plus the profile C⊥ and cluster the simulated data using the one-stage ORICC algorithm. Note that the set of candidate profiles does not contain the true one C∧∧, but C∧∧ may be viewed as a special case of C⊥. Let γ⊥ denote the proportion of genes clustered to the profile C⊥, and define the detection error as 1 - γ⊥. The simulation results are summarized in Figure 12. It shows that the detection error decreases rapidly as the number of replicates M increases. With 5 replicates, the detection error is below 30%, and with 10 replicates, it goes down to below 5%. This indicates that it is quite safe to apply the ORICC algorithm even if some true profile is missing from the candidate profiles but a more comprehensive profile is considered.


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)

Simulation 4: Detection error rate. This figure plots the detection error rate of the one-stage ORICC algorithm for a true profile that is not explicitly specified in the candidate profiles.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 12: Simulation 4: Detection error rate. This figure plots the detection error rate of the one-stage ORICC algorithm for a true profile that is not explicitly specified in the candidate profiles.
Mentions: We generate a data set containing 2000 genes from this profile. At each time point t, we generate M replicates for each gene's expression level from normal distributions with means μt and variance 0.5. Then, we consider candidate profiles being C1, C2, C4 and C9 plus the profile C⊥ and cluster the simulated data using the one-stage ORICC algorithm. Note that the set of candidate profiles does not contain the true one C∧∧, but C∧∧ may be viewed as a special case of C⊥. Let γ⊥ denote the proportion of genes clustered to the profile C⊥, and define the detection error as 1 - γ⊥. The simulation results are summarized in Figure 12. It shows that the detection error decreases rapidly as the number of replicates M increases. With 5 replicates, the detection error is below 30%, and with 10 replicates, it goes down to below 5%. This indicates that it is quite safe to apply the ORICC algorithm even if some true profile is missing from the candidate profiles but a more comprehensive profile is considered.

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