Sciweavers

Share
BMCBI
2007

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

11 years 8 months ago
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
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 pr...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Curtis Huttenhower, Avi I. Flamholz, Jessica N. Landis, Sauhard Sahi, Chad L. Myers, Kellen L. Olszewski, Matthew A. Hibbs, Nathan O. Siemers, Olga G. Troyanskaya, Hilary A. Coller
Comments (0)
books