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Measuring similarities between gene expression profiles through new data transformations.

Kim K, Zhang S, Jiang K, Cai L, Lee IB, Feldman LJ, Huang H - BMC Bioinformatics (2007)

Bottom Line: Applying these algorithms to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA dataset, and a maize root affymetrix microarray dataset, we found from the results that the algorithm associated with the first feature space, named TransChisq, showed clear advantages over other methods.The proposed TransChisq is very promising in capturing meaningful gene expression clusters.The clustering algorithms are available upon request.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of California, Berkeley, USA. kpkim@stat.berkeley.edu <kpkim@stat.berkeley.edu>

ABSTRACT

Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space.

Results: We explored several different transformation schemes to construct the feature spaces that include a space whose features are determined by the mutual differences of the original expression components, a space derived from a parametric covariance matrix, and the principal component space in traditional PCA analysis. The former two are the newly proposed and the latter is explored for comparison purposes. The new measures we defined in these feature spaces were employed in a K-means clustering procedure to perform analyses. Applying these algorithms to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA dataset, and a maize root affymetrix microarray dataset, we found from the results that the algorithm associated with the first feature space, named TransChisq, showed clear advantages over other methods.

Conclusion: The proposed TransChisq is very promising in capturing meaningful gene expression clusters. This study also demonstrates the importance of data transformations in defining an efficient distance measure. Our method should provide new insights in analyzing gene expression data. The clustering algorithms are available upon request.

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Expression patterns of the 39 representative genes in the yeast sporulation data. These 39 representative genes represent seven expression patterns in the yeast sporulation data. The figure shows the average expression profile for each pattern.
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Figure 4: Expression patterns of the 39 representative genes in the yeast sporulation data. These 39 representative genes represent seven expression patterns in the yeast sporulation data. The figure shows the average expression profile for each pattern.

Mentions: To further demonstrate how effective TransChisq is in clustering genes with characterized patterns in a microarray analysis, we applied TransChisq to a microarray yeast sporulation dataset [22]. Chu et al. measured gene expressions in the budding yeast Saccharomyces cerevisiae at seven time points during sporulation using spotted microarrays, and identified seven distinct temporal patterns of induction [22]. 39 representative genes were used to define the model expression profile for each pattern. Based on their properties, the seven patterns are designated as Metabolic, Early I, Early II, Early-Mid, Middle, Mid-Late and Late. The average expression profiles for these seven patterns are presented in Figure 4. The genes in Early I, Early II, Middle, Mid-Late and Late initiates induction of expression at 0.5 h, 2 h, 5 h, 7 h and 9 h, respectively, and sustains expression through the rest of the time course. The expression of metabolic genes is also induced at 0.5 h as in Early I, but decays afterwards. The expression of genes in Early-Mid is induced not only at the 0.5 h and 2 h as in Early genes, but also at 5 h and 7 h, as in the Middle and Mid-Late genes. This data structure made it difficult to separate the Early-Mid genes from others. The direct clustering analyses using PearsonC or Eucli were not successful.


Measuring similarities between gene expression profiles through new data transformations.

Kim K, Zhang S, Jiang K, Cai L, Lee IB, Feldman LJ, Huang H - BMC Bioinformatics (2007)

Expression patterns of the 39 representative genes in the yeast sporulation data. These 39 representative genes represent seven expression patterns in the yeast sporulation data. The figure shows the average expression profile for each pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1804284&req=5

Figure 4: Expression patterns of the 39 representative genes in the yeast sporulation data. These 39 representative genes represent seven expression patterns in the yeast sporulation data. The figure shows the average expression profile for each pattern.
Mentions: To further demonstrate how effective TransChisq is in clustering genes with characterized patterns in a microarray analysis, we applied TransChisq to a microarray yeast sporulation dataset [22]. Chu et al. measured gene expressions in the budding yeast Saccharomyces cerevisiae at seven time points during sporulation using spotted microarrays, and identified seven distinct temporal patterns of induction [22]. 39 representative genes were used to define the model expression profile for each pattern. Based on their properties, the seven patterns are designated as Metabolic, Early I, Early II, Early-Mid, Middle, Mid-Late and Late. The average expression profiles for these seven patterns are presented in Figure 4. The genes in Early I, Early II, Middle, Mid-Late and Late initiates induction of expression at 0.5 h, 2 h, 5 h, 7 h and 9 h, respectively, and sustains expression through the rest of the time course. The expression of metabolic genes is also induced at 0.5 h as in Early I, but decays afterwards. The expression of genes in Early-Mid is induced not only at the 0.5 h and 2 h as in Early genes, but also at 5 h and 7 h, as in the Middle and Mid-Late genes. This data structure made it difficult to separate the Early-Mid genes from others. The direct clustering analyses using PearsonC or Eucli were not successful.

Bottom Line: Applying these algorithms to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA dataset, and a maize root affymetrix microarray dataset, we found from the results that the algorithm associated with the first feature space, named TransChisq, showed clear advantages over other methods.The proposed TransChisq is very promising in capturing meaningful gene expression clusters.The clustering algorithms are available upon request.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of California, Berkeley, USA. kpkim@stat.berkeley.edu <kpkim@stat.berkeley.edu>

ABSTRACT

Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space.

Results: We explored several different transformation schemes to construct the feature spaces that include a space whose features are determined by the mutual differences of the original expression components, a space derived from a parametric covariance matrix, and the principal component space in traditional PCA analysis. The former two are the newly proposed and the latter is explored for comparison purposes. The new measures we defined in these feature spaces were employed in a K-means clustering procedure to perform analyses. Applying these algorithms to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA dataset, and a maize root affymetrix microarray dataset, we found from the results that the algorithm associated with the first feature space, named TransChisq, showed clear advantages over other methods.

Conclusion: The proposed TransChisq is very promising in capturing meaningful gene expression clusters. This study also demonstrates the importance of data transformations in defining an efficient distance measure. Our method should provide new insights in analyzing gene expression data. The clustering algorithms are available upon request.

Show MeSH
Related in: MedlinePlus