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

BioMed Research International
Kai WangTianwei Yu

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 perfor...Continue Reading

References

Oct 20, 2004·Proceedings of the National Academy of Sciences of the United States of America·Ker-Chau LiTianwei Yu
Nov 14, 2006·Bioinformatics·S Falcon, R Gentleman
Nov 21, 2007·Bioinformatics·Peter LangfelderSteve Horvath
Aug 25, 2010·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Tianwei YuWei Sun
Nov 24, 2011·Nucleic Acids Research·J Michael CherryEdith D Wong
Mar 23, 2012·Nature Reviews. Molecular Cell Biology·Gary J PattiGary Siuzdak
Dec 28, 2012·Nature Reviews. Genetics·Johan Rung, Alvis Brazma
Dec 18, 2013·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Tianwei Yu, Hesen Peng
May 20, 2015·The Annals of Applied Statistics·Yize ZhaoTianwei Yu

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