dorsal/arxiv
View SchemaSuper-paramagnetic clustering of yeast gene expression profiles
| Authors | G. Getz, E. Levine, E. Domany, M. Q. Zhang |
|---|---|
| Categories | |
| ArXiv ID | physics/9911038 |
| URL | https://arxiv.org/abs/physics/9911038 |
| DOI | 10.1016/S0378-4371(99)00524-5 |
| Journal | Physica A 279 (2000) 457--464 |
Abstract
High-density DNA arrays, used to monitor gene expression at a genomic scale, have produced vast amounts of information which require the development of efficient computational methods to analyze them. The important first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of a novel clustering algorithm, Super-Paramagnetic Clustering (SPC) to analysis of gene expression profiles that were generated recently during a study of the yeast cell cycle. SPC was used to organize genes into biologically relevant clusters that are suggestive for their co-regulation. Some of the advantages of SPC are its robustness against noise and initialization, a clear signature of cluster formation and splitting, and an unsupervised self-organized determination of the number of clusters at each resolution. Our analysis revealed interesting correlated behavior of several groups of genes which has not been previously identified.
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"abstract": "High-density DNA arrays, used to monitor gene expression at a genomic scale,\nhave produced vast amounts of information which require the development of\nefficient computational methods to analyze them. The important first step is to\nextract the fundamental patterns of gene expression inherent in the data. This\npaper describes the application of a novel clustering algorithm,\nSuper-Paramagnetic Clustering (SPC) to analysis of gene expression profiles\nthat were generated recently during a study of the yeast cell cycle. SPC was\nused to organize genes into biologically relevant clusters that are suggestive\nfor their co-regulation. Some of the advantages of SPC are its robustness\nagainst noise and initialization, a clear signature of cluster formation and\nsplitting, and an unsupervised self-organized determination of the number of\nclusters at each resolution. Our analysis revealed interesting correlated\nbehavior of several groups of genes which has not been previously identified.",
"arxiv_id": "physics/9911038",
"authors": [
"G. Getz",
"E. Levine",
"E. Domany",
"M. Q. Zhang"
],
"categories": [
"physics.bio-ph",
"q-bio.QM"
],
"doi": "10.1016/S0378-4371(99)00524-5",
"journal_ref": "Physica A 279 (2000) 457--464",
"title": "Super-paramagnetic clustering of yeast gene expression profiles",
"url": "https://arxiv.org/abs/physics/9911038"
},
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