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Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.

Huttenhower C, Flamholz AI, Landis JN, Sahi S, Myers CL, Olszewski KL, Hibbs MA, Siemers NO, Troyanskaya OG, Coller HA - BMC Bioinformatics (2007)

Bottom Line: An important initial step in the analysis of microarray data is clustering of genes with similar behavior.This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered.It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>

ABSTRACT

Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).

Results: We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.

Conclusion: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

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Functional diversity of clustering algorithms. An evaluation of each clustering algorithm's ability to detect the 88 biological processes for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods.
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Figure 5: Functional diversity of clustering algorithms. An evaluation of each clustering algorithm's ability to detect the 88 biological processes for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods.

Mentions: NNN clusters tend to describe a broader array of biological processes than those of previous methods, and they often relate functional information that might otherwise remain undetected. Figure 5 summarizes each clustering algorithm's maximum performance for each biological function across all six data sets. Of the 88 functions evaluated in this manner, 40 are predicted at biologically uninformative levels (AUC < 0.65) by previous methods. NNN improves 18 of these functions to an AUC greater than 0.65 (as high as 0.9 in several cases). It further improves performance in an additional 21 functions also predicted well (AUC > 0.65) by other algorithms. In the concatenated data, NNN improved the best AUC above 0.65 in 14 functions and was the best predictor of an additional 10 beyond those. As Figure 5 indicates, NNN is generally able to recover information about more biological processes with higher precision than other clustering algorithms.


Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.

Huttenhower C, Flamholz AI, Landis JN, Sahi S, Myers CL, Olszewski KL, Hibbs MA, Siemers NO, Troyanskaya OG, Coller HA - BMC Bioinformatics (2007)

Functional diversity of clustering algorithms. An evaluation of each clustering algorithm's ability to detect the 88 biological processes for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1941745&req=5

Figure 5: Functional diversity of clustering algorithms. An evaluation of each clustering algorithm's ability to detect the 88 biological processes for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods.
Mentions: NNN clusters tend to describe a broader array of biological processes than those of previous methods, and they often relate functional information that might otherwise remain undetected. Figure 5 summarizes each clustering algorithm's maximum performance for each biological function across all six data sets. Of the 88 functions evaluated in this manner, 40 are predicted at biologically uninformative levels (AUC < 0.65) by previous methods. NNN improves 18 of these functions to an AUC greater than 0.65 (as high as 0.9 in several cases). It further improves performance in an additional 21 functions also predicted well (AUC > 0.65) by other algorithms. In the concatenated data, NNN improved the best AUC above 0.65 in 14 functions and was the best predictor of an additional 10 beyond those. As Figure 5 indicates, NNN is generally able to recover information about more biological processes with higher precision than other clustering algorithms.

Bottom Line: An important initial step in the analysis of microarray data is clustering of genes with similar behavior.This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered.It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>

ABSTRACT

Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).

Results: We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.

Conclusion: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

Show MeSH
Related in: MedlinePlus