scholarly journals CLUSTERING BIOLOGICAL ANNOTATIONS AND GENE EXPRESSION DATA TO IDENTIFY PUTATIVELY CO-REGULATED BIOLOGICAL PROCESSES

2006 ◽  
Vol 04 (04) ◽  
pp. 833-852 ◽  
Author(s):  
CORNELIU HENEGAR ◽  
RAFFAELLA CANCELLO ◽  
SOPHIE ROME ◽  
HUBERT VIDAL ◽  
KARINE CLÉMENT ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulation Of Gene Expression ◽  
Muscular Contraction ◽  
Supplementary Information ◽  
Biological Processes ◽  
Expression Data ◽  
Biological Interactions ◽  
Microarray Gene Expression ◽  
Data Set

Motivation: Functional profiling is a key step of microarray gene expression data analysis. Identifying co-regulated biological processes could help for better understanding of underlying biological interactions within the studied biological frame. Results: We present herein an original approach designed to search for putatively co-regulated biological processes sharing a significant number of co-expressed genes. An R language implementation named "FunCluster" was built and tested on two gene expression data sets. A discriminatory functional analysis of the first data set, related to experiments performed on separated adipocytes and stroma vascular fraction cells of human white adipose tissue, highlighted the prevalent role of nonadipose cells in the synthesis of inflammatory and immunity molecules in human adiposity. On the second data set, resulting from a model investigating insulin coordinated regulation of gene expression in human skeletal muscle, FunCluster analysis spotlighted novel functional classes of putatively co-regulated biological processes related to protein metabolism and the regulation of muscular contraction. Availability: Supplementary information about the FunCluster tool is available on-line at .

ADDITIVE RISK ANALYSIS OF MICROARRAY GENE EXPRESSION DATA VIA CORRELATION PRINCIPAL COMPONENT REGRESSION

2010 ◽  
Vol 08 (04) ◽  
pp. 645-659 ◽  
Author(s):  
YICHUAN ZHAO ◽  
GUOSHEN WANG
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Risk Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Additive Risk

In order to predict future patients' survival time based on their microarray gene expression data, one interesting question is how to relate genes to survival outcomes. In this paper, by applying a semi-parametric additive risk model in survival analysis, we propose a new approach to conduct a careful analysis of gene expression data with the focus on the model's predictive ability. In the proposed method, we apply the correlation principal component regression to deal with right censoring survival data under the semi-parametric additive risk model frame with high-dimensional covariates. We also employ the time-dependent area under the receiver operating characteristic curve and root mean squared error for prediction to assess how well the model can predict the survival time. Furthermore, the proposed method is able to identify significant genes, which are significantly related to the disease. Finally, the proposed useful approach is illustrated by the diffuse large B-cell lymphoma data set and breast cancer data set. The results show that the model fits the data sets very well.

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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