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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| Veröffentlicht in: | BioMed research international Jg. 2015; H. 2015; S. 1 - 10 |
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| Sprache: | Englisch |
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Cairo, Egypt
Hindawi Publishing Corporation
01.01.2015
John Wiley & Sons, Inc |
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| ISSN: | 2314-6133, 2314-6141, 2314-6141 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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.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. |
| Audience | Academic |
| Author | Lu, Jianwei Zhao, Qing Wang, Kai Yu, Tianwei |
| AuthorAffiliation | 2 School of Software Engineering, Tongji University, Shanghai 200092, China 4 Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA 3 The Advanced Institute of Translational Medicine and Department of Gastroenterology, . Shanghai Tenth People's Hospital, Tongji University, Shanghai 200092, China 1 Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA |
| AuthorAffiliation_xml | – name: 1 Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA – name: 4 Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA – name: 2 School of Software Engineering, Tongji University, Shanghai 200092, China – name: 3 The Advanced Institute of Translational Medicine and Department of Gastroenterology, . Shanghai Tenth People's Hospital, Tongji University, Shanghai 200092, China |
| Author_xml | – sequence: 1 fullname: Yu, Tianwei – sequence: 2 fullname: Lu, Jianwei – sequence: 3 fullname: Zhao, Qing – sequence: 4 fullname: Wang, Kai |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26339652$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1155_2022_4740173 |
| Cites_doi | 10.1287/ijoc.15.1.82.15157 10.1186/1471-2164-12-563 10.1007/BF01908075 10.1186/1471-2105-11-440 10.1007/s11222-008-9090-y 10.1038/75556 10.1093/nar/gkr1029 10.1214/14-aoas719 10.1186/1753-6561-5-s2-s3 10.1109/TCBB.2013.99 10.1093/bioinformatics/btm563 10.1093/bioinformatics/btl567 10.1109/tkde.2004.68 10.1109/34.841759 10.1109/tcbb.2010.73 10.1038/nrg3394 10.1137/1.9780898718348 10.1073/pnas.0402962101 10.1016/j.ygeno.2011.09.001 10.1186/1471-2105-12-197 10.1091/mbc.9.12.3273 10.1038/nrm3314 |
| ContentType | Journal Article |
| Copyright | Copyright © 2015 Kai Wang et al. COPYRIGHT 2015 John Wiley & Sons, Inc. Copyright © 2015 Kai Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2015 Kai Wang et al. 2015 |
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| SubjectTerms | 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 |
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| Title | K -Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data |
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