ArticleBMC bioinformatics2007
Difference-based clustering of short time-course microarray data with replicates.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates.BMC immunology · 2010Article
- Computational methods for analyzing dynamic regulatory networks.Methods in molecular biology (Clifton, N.J.) · 2010Review
- Functional assessment of time course microarray data.BMC bioinformatics · 2009Article
- Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.BMC bioinformatics · 2009Article
- Short time-series microarray analysis: methods and challenges.BMC systems biology · 2008Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically.
resultsWe propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods.
conclusionsAssessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.
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Registered trials
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