Clustering analysis for gene expression data

1999 ◽  
Author(s):  
Yidong Chen ◽  
Olga Ermolaeva ◽  
Michael L. Bittner ◽  
Paul S. Meltzer ◽  
Jeffrey M. Trent ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Expression Data

scholarly journals Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

2003 ◽  
Vol 19 (9) ◽  
pp. 1079-1089 ◽  
Author(s):  
G. Getz ◽  
H. Gal ◽  
I. Kela ◽  
D. A. Notterman ◽  
E. Domany
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Colon Cancer ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Cancer Gene ◽  
Expression Data

scholarly journals Clustering analysis of tumor metabolic networks

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Ichcha Manipur ◽  
Ilaria Granata ◽  
Lucia Maddalena ◽  
Mario R. Guarracino
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Biological Networks ◽  
Clustering Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Computational Time ◽  
Expression Data ◽  
Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Clustering Analysis of Gene Expression Data based on Semi-supervised Visual Clustering Algorithm

2006 ◽  
Vol 10 (11) ◽  
pp. 981-993 ◽  
Author(s):  
Fu-lai Chung ◽  
Shitong Wang ◽  
Zhaohong Deng ◽  
Chen Shu ◽  
D. Hu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Expression Data ◽  
Visual Clustering

scholarly journals Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Li-Ping Tian ◽  
Li-Zhi Liu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Nonlinear Model ◽  
Clustering Analysis ◽  
Time Course ◽  
Disease Diagnosis ◽  
Expression Data ◽  
Analysis Method ◽  
Significance Analysis ◽  
Analysis Methods

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.

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