scholarly journals A review of independent component analysis application to microarray gene expression data

BioTechniques ◽  
2008 ◽  
Vol 45 (5) ◽  
pp. 501-520 ◽  
Author(s):  
Wei Kong ◽  
Charles R. Vanderburg ◽  
Hiromi Gunshin ◽  
Jack T. Rogers ◽  
Xudong Huang
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Gene Expression Data ◽  
Component Analysis ◽  
Microarray Gene Expression Data ◽  
Independent Component ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Analysis Application ◽  
Microarray Gene

TUMOR CLASSIFICATION BASED ON INDEPENDENT COMPONENT ANALYSIS

2006 ◽  
Vol 20 (02) ◽  
pp. 297-310 ◽  
Author(s):  
CHUN-HOU ZHENG ◽  
YAN CHEN ◽  
XIU-XIA LI ◽  
YI-XUE LI ◽  
YUN-PING ZHU
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Component Analysis ◽  
Tumor Classification ◽  
Independent Component ◽  
Support Vector ◽  
Expression Data ◽  
Microarray Gene Expression

This paper proposes a new method for tumor classification using gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are selected using t-statistics. Secondly, the selected genes are modeled by Independent Component Analysis (ICA). Finally, Support Vector Machine (SVM) is used to classify the modeling data. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Qiang Zhao ◽  
Jianguo Sun
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
New Approach ◽  
Microarray Gene

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.

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