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A comparison of four clustering methods for brain expression microarray data.

Richards AL, Holmans P, O'Donovan MC, Owen MJ, Jones L - BMC Bioinformatics (2008)

Bottom Line: The results are compared on speed, gene coverage and GO enrichment.Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters.In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk

ABSTRACT

Background: DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.

Results: k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.

Conclusion: Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.

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Related in: MedlinePlus

Flowchart summarising the method used by CRC. One run through this flowchart equates to a single chain in CRC, with several chains being run in parallel. The number of parallel chains and the maximum number of iterations are user-defined parameters.
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Figure 2: Flowchart summarising the method used by CRC. One run through this flowchart equates to a single chain in CRC, with several chains being run in parallel. The number of parallel chains and the maximum number of iterations are user-defined parameters.

Mentions: The procedure is outlined in Fig. 2. Each iteration of the flowchart in Fig. 2 can be considered a Markov chain process. CRC runs a number of these chains in parallel (set by the user – 10 is the recommended amount), and reports the highest likelihood cluster set as the final output. The chains are also limited to a certain number of iterations through the flowchart before reporting their clusters. This is another parameter decided by the user, and is recommended to be set at 20. Finally, a probability cut-off can be input, which determines how high the likelihood of a gene being a member of a cluster needs to be in order for it to be included in the final output. In practice, most genes are members of their cluster with probability 1, so this removes few genes.


A comparison of four clustering methods for brain expression microarray data.

Richards AL, Holmans P, O'Donovan MC, Owen MJ, Jones L - BMC Bioinformatics (2008)

Flowchart summarising the method used by CRC. One run through this flowchart equates to a single chain in CRC, with several chains being run in parallel. The number of parallel chains and the maximum number of iterations are user-defined parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655095&req=5

Figure 2: Flowchart summarising the method used by CRC. One run through this flowchart equates to a single chain in CRC, with several chains being run in parallel. The number of parallel chains and the maximum number of iterations are user-defined parameters.
Mentions: The procedure is outlined in Fig. 2. Each iteration of the flowchart in Fig. 2 can be considered a Markov chain process. CRC runs a number of these chains in parallel (set by the user – 10 is the recommended amount), and reports the highest likelihood cluster set as the final output. The chains are also limited to a certain number of iterations through the flowchart before reporting their clusters. This is another parameter decided by the user, and is recommended to be set at 20. Finally, a probability cut-off can be input, which determines how high the likelihood of a gene being a member of a cluster needs to be in order for it to be included in the final output. In practice, most genes are members of their cluster with probability 1, so this removes few genes.

Bottom Line: The results are compared on speed, gene coverage and GO enrichment.Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters.In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk

ABSTRACT

Background: DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.

Results: k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.

Conclusion: Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.

Show MeSH
Related in: MedlinePlus