Nearest Neighbor Networks: clustering expression data based on gene neighborhoods |
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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 |
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Institution: | (1) Department of Computer Science, Princeton University, Princeton, NJ 08544, USA;(2) Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA;(3) Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA;(4) Bristol-Myers Squibb, 31 Pennington-Rocky Hill Road, Pennington, NJ 08534, USA |
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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). |
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