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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.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.Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets.

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 ISA. tG and tC are user-defined threshold parameters. They determine how great the level of expression for a gene or sample (defined in standard deviations from the weighted mean of all genes over those samples, or all samples over those genes) needs to be for selection in the cluster. Higher values lead to more, smaller clusters, lower values to fewer, larger clusters. A preliminary run at a low number of iterations, with a wide range of values for tG and tC, is used to determine a sensible range of tG and tC values for use in the main run.
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Figure 3: Flowchart summarising the method used by ISA. tG and tC are user-defined threshold parameters. They determine how great the level of expression for a gene or sample (defined in standard deviations from the weighted mean of all genes over those samples, or all samples over those genes) needs to be for selection in the cluster. Higher values lead to more, smaller clusters, lower values to fewer, larger clusters. A preliminary run at a low number of iterations, with a wide range of values for tG and tC, is used to determine a sensible range of tG and tC values for use in the main run.

Mentions: ISA is a biclustering method – it clusters both rows and columns of the dataset, here the genes and the specific samples they come from [12,13]. This allows ISA to focus on subsets of samples with good signal for the genes of the cluster, reducing the amount of noise (see Fig. 3). Unlike k-means and CRC, it is not a single-cluster membership method: it allows genes and samples to belong to multiple clusters, and does not have to put every gene into a cluster. A high proportion of its clusters were found to be significantly enriched for one or more GO terms in yeast data[4].


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 ISA. tG and tC are user-defined threshold parameters. They determine how great the level of expression for a gene or sample (defined in standard deviations from the weighted mean of all genes over those samples, or all samples over those genes) needs to be for selection in the cluster. Higher values lead to more, smaller clusters, lower values to fewer, larger clusters. A preliminary run at a low number of iterations, with a wide range of values for tG and tC, is used to determine a sensible range of tG and tC values for use in the main run.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Flowchart summarising the method used by ISA. tG and tC are user-defined threshold parameters. They determine how great the level of expression for a gene or sample (defined in standard deviations from the weighted mean of all genes over those samples, or all samples over those genes) needs to be for selection in the cluster. Higher values lead to more, smaller clusters, lower values to fewer, larger clusters. A preliminary run at a low number of iterations, with a wide range of values for tG and tC, is used to determine a sensible range of tG and tC values for use in the main run.
Mentions: ISA is a biclustering method – it clusters both rows and columns of the dataset, here the genes and the specific samples they come from [12,13]. This allows ISA to focus on subsets of samples with good signal for the genes of the cluster, reducing the amount of noise (see Fig. 3). Unlike k-means and CRC, it is not a single-cluster membership method: it allows genes and samples to belong to multiple clusters, and does not have to put every gene into a cluster. A high proportion of its clusters were found to be significantly enriched for one or more GO terms in yeast data[4].

Bottom Line: The results are compared on speed, gene coverage and GO enrichment.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.Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets.

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