Comparative Analysis of Clustering Methods for Microarray Data

2008 ◽  
pp. 27-50 ◽  
Author(s):  
Dongxiao Zhu ◽  
Mary-Lee Dequeant ◽  
Hua Li
Keyword(s):  
Comparative Analysis ◽  
Microarray Data ◽  
Clustering Methods

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

scholarly journals Comparative analysis of clustering methods for gene expression time course data

2004 ◽  
Vol 27 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ivan G. Costa ◽  
Francisco de A. T. de Carvalho ◽  
Marcílio C. P. de Souto
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Time Course ◽  
Clustering Methods ◽  
Time Course Data ◽  
Expression Time

SPECTRAL CLUSTERING ON GENE EXPRESSION PROFILE TO IDENTIFY CANCER TYPES OR SUBTYPES

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Ang Jun Chin ◽  
Andri Mirzal ◽  
Habibollah Haron
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Microarray Data ◽  
Spectral Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Cancer Types ◽  
Microarray Gene

Gene expression profile is eminent for its broad applications and achievements in disease discovery and analysis, especially in cancer research. Spectral clustering is robust to irrelevant features which are appropriated for gene expression analysis. However, previous works show that performance comparison with other clustering methods is limited and only a few microarray data sets were analyzed in each study. In this study, we demonstrate the use of spectral clustering in identifying cancer types or subtypes from microarray gene expression profiling. Spectral clustering was applied to eleven microarray data sets and its clustering performances were compared with the results in the literature. Based on the result, overall the spectral clustering slightly outperformed the corresponding results in the literature. The spectral clustering can also offer more stable clustering performances as it has smaller standard deviation value. Moreover, out of eleven data sets the spectral clustering outperformed the corresponding methods in the literature for six data sets. So, it can be stated that the spectral clustering is a promising method in identifying the cancer types or subtypes for microarray gene expression data sets.

scholarly journals ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD

2021 ◽  
Vol 2021 (60) ◽  
pp. 51-57
Author(s):  
P.V. Shymaniuk ◽  
◽  
V.O. Miroshnyk ◽  
Keyword(s):  
United States ◽  
Time Series ◽  
Comparative Analysis ◽  
Decomposition Methods ◽  
The United States ◽  
Data Validation ◽  
Clustering Methods ◽  
Short Term ◽  
Northwestern Region ◽  
Short Term Forecasting

A comparative analysis of clustering methods was performed to identify gaps and anomalous values in the data. Data from the northwestern region of the United States were used for evaluation. According to the analysis results, it was found that the use of the DBSCAN method leads to a much smaller number of false positives. An algorithm for two-stage data validation using clustering and time series decomposition methods is proposed. Ref.9, fig. 3, tables 3.

Algorithmic and Complexity Issues of Three Clustering Methods in Microarray Data Analysis

Algorithmica ◽  
2007 ◽  
Vol 48 (2) ◽  
pp. 203-219 ◽  
Author(s):  
Jinsong Tan ◽  
Kok Seng Chua ◽  
Louxin Zhang ◽  
Song Zhu
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Clustering Methods

scholarly journals A comparison of four clustering methods for brain expression microarray data

2008 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander L Richards ◽  
Peter Holmans ◽  
Michael C O'Donovan ◽  
Michael J Owen ◽  
Lesley Jones
Keyword(s):  
Microarray Data ◽  
Clustering Methods ◽  
Expression Microarray ◽  
Brain Expression
Sign in / Sign up

Export Citation Format

Share Document