K -Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data

With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS), it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden pat...

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Vydané v:BioMed research international Ročník 2015; číslo 2015; s. 1 - 10
Hlavní autori: Yu, Tianwei, Lu, Jianwei, Zhao, Qing, Wang, Kai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
John Wiley & Sons, Inc
Predmet:
Algorithms
Bioinformatics
Cluster Analysis
Clustering
Gene Expression
Gene Expression Profiling - statistics & numerical data
Genomics
High-Throughput Nucleotide Sequencing - statistics & numerical data
Humans
Liquid chromatography
Mass spectrometry
Methods
Microarray Analysis - statistics & numerical data
Models, Statistical
Random variables
Statistical inference
Traveling salesman problem
Atlanta Georgia
United States > US
China
ISSN:2314-6133, 2314-6141, 2314-6141
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Shrnutí:With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS), it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden patterns and understanding the massive data is the application of clustering techniques. Nonlinear relations, which were mostly unutilized in contrast to linear correlations, are prevalent in high-throughput data. In many cases, nonlinear relations can model the biological relationship more precisely and reflect critical patterns in the biological systems. Using the general dependency measure, Distance Based on Conditional Ordered List (DCOL) that we introduced before, we designed the nonlinear K-profiles clustering method, which can be seen as the nonlinear counterpart of the K-means clustering algorithm. The method has a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles. Results from extensive simulation studies showed that K-profiles clustering not only outperformed traditional linear K-means algorithm, but also presented significantly better performance over our previous General Dependency Hierarchical Clustering (GDHC) algorithm. We further analyzed a gene expression dataset, on which K-profile clustering generated biologically meaningful results.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Fang-Xiang Wu
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2015/918954