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Application of oligonucleotide microarrays for bacterial source tracking of environmental Enterococcus sp. isolates.

Indest KJ, Betts K, Furey JS - Int J Environ Res Public Health (2005)

Bottom Line: Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated.The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors.The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Engineer Research and Development Center, Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA. indestk@wes.army.mil

ABSTRACT
In an effort towards adapting new and defensible methods for assessing and managing the risk posed by microbial pollution, we evaluated the utility of oligonucleotide microarrays for bacterial source tracking (BST) of environmental Enterococcus sp. isolates derived from various host sources. Current bacterial source tracking approaches rely on various phenotypic and genotypic methods to identify sources of bacterial contamination resulting from point or non-point pollution. For this study Enterococcus sp. isolates originating from deer, bovine, gull, and human sources were examined using microarrays. Isolates were subjected to Box PCR amplification and the resulting amplification products labeled with Cy5. Fluorescent-labeled templates were hybridized to in-house constructed nonamer oligonucleotide microarrays consisting of 198 probes. Microarray hybridization profiles were obtained using the ArrayPro image analysis software. Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors. A Soft Independent Modeling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

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Related in: MedlinePlus

Principal Components Analysis of normalized microarray spot intensities of replicates of 17 environmental isolates of Enterococcus sp., colored by host origin: deer is red, bovine is yellow, human is green, gull is purple. For this 3D view only the first 3 components can be plotted, but clustering is evident.
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f5-ijerph-02-00175: Principal Components Analysis of normalized microarray spot intensities of replicates of 17 environmental isolates of Enterococcus sp., colored by host origin: deer is red, bovine is yellow, human is green, gull is purple. For this 3D view only the first 3 components can be plotted, but clustering is evident.

Mentions: The dendrogram of a complete Euclidean distance Hierarchical Cluster Analysis (HCA) did not project good origin-specific clustering of the isolates. In particular, the bovine-origin replicates were spread among several clusters (example part of dendrogram Fig. 4). A K-Nearest Neighbour classification confirmed the HCA, misclassifying 8% of the deer, 16% of the human, and 50% of the gull isolates as bovine isolates. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors (Fig. 5). A Soft Independent Modelling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. Numerical descriptions of the SIM classification model for bovine-origin Enterococcus sp. are presented in Table II. These factors describe the multidimensional subspace within the PCA projection in which the various microarray source profiles exist. Factor numbers indicate the relative linear weights of each probe in each factor. For instance probes 2 and 16 have the highest weights for the most important factor, Factor 1, which accounts for 30% of the variability. Thus for this set of isolates, SIM classifications based on 5 factors for each class and 5 linear combinations of the 45 probes sufficed to distinguish the origins of Enterococcus sp. isolates.


Application of oligonucleotide microarrays for bacterial source tracking of environmental Enterococcus sp. isolates.

Indest KJ, Betts K, Furey JS - Int J Environ Res Public Health (2005)

Principal Components Analysis of normalized microarray spot intensities of replicates of 17 environmental isolates of Enterococcus sp., colored by host origin: deer is red, bovine is yellow, human is green, gull is purple. For this 3D view only the first 3 components can be plotted, but clustering is evident.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814713&req=5

f5-ijerph-02-00175: Principal Components Analysis of normalized microarray spot intensities of replicates of 17 environmental isolates of Enterococcus sp., colored by host origin: deer is red, bovine is yellow, human is green, gull is purple. For this 3D view only the first 3 components can be plotted, but clustering is evident.
Mentions: The dendrogram of a complete Euclidean distance Hierarchical Cluster Analysis (HCA) did not project good origin-specific clustering of the isolates. In particular, the bovine-origin replicates were spread among several clusters (example part of dendrogram Fig. 4). A K-Nearest Neighbour classification confirmed the HCA, misclassifying 8% of the deer, 16% of the human, and 50% of the gull isolates as bovine isolates. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors (Fig. 5). A Soft Independent Modelling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. Numerical descriptions of the SIM classification model for bovine-origin Enterococcus sp. are presented in Table II. These factors describe the multidimensional subspace within the PCA projection in which the various microarray source profiles exist. Factor numbers indicate the relative linear weights of each probe in each factor. For instance probes 2 and 16 have the highest weights for the most important factor, Factor 1, which accounts for 30% of the variability. Thus for this set of isolates, SIM classifications based on 5 factors for each class and 5 linear combinations of the 45 probes sufficed to distinguish the origins of Enterococcus sp. isolates.

Bottom Line: Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated.The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors.The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Engineer Research and Development Center, Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA. indestk@wes.army.mil

ABSTRACT
In an effort towards adapting new and defensible methods for assessing and managing the risk posed by microbial pollution, we evaluated the utility of oligonucleotide microarrays for bacterial source tracking (BST) of environmental Enterococcus sp. isolates derived from various host sources. Current bacterial source tracking approaches rely on various phenotypic and genotypic methods to identify sources of bacterial contamination resulting from point or non-point pollution. For this study Enterococcus sp. isolates originating from deer, bovine, gull, and human sources were examined using microarrays. Isolates were subjected to Box PCR amplification and the resulting amplification products labeled with Cy5. Fluorescent-labeled templates were hybridized to in-house constructed nonamer oligonucleotide microarrays consisting of 198 probes. Microarray hybridization profiles were obtained using the ArrayPro image analysis software. Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors. A Soft Independent Modeling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

Show MeSH
Related in: MedlinePlus