Dual analysis of DNA microarrays

Cagatay Turkay, Julius Parulek, Helwig Hauser

INPROCEEDINGS, Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, 2012

• paper

Abstract

Microarray data represents the expression levels of genes for different samples and for different conditions. It has been a central topic in bioinformatics research for a long time already. Researchers try to discover groups of genes that are responsible for specific biological processes. Statistical analysis tools and visualizations have been widely used in the analysis of microarray data. Researchers try to build hypotheses on both the genes and the samples. Therefore, such analyses require the joint exploration of the genes and the samples. However, current methods in interactive visual analysis fail to provide the necessary mechanisms for this joint analysis. In this paper, we propose an interactive visual analysis framework that enables the dual analysis of the samples and the genes through the use of integrated statistical tools. We introduce a set of specialized views and a detailed analysis procedure to describe the utilization of our framework.

Published

Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies

• Series: i-KNOW '12
• Pages: 26:1–26:8
• Location: Graz, Austria

Media

BibTeX

@inproceedings{Turkay2012DualDNA,
 author = {Cagatay Turkay and Julius Parulek and Helwig Hauser},
 title = {Dual analysis of DNA microarrays},
 booktitle = {Proceedings of the 12th International Conference on Knowledge Management 
  and Knowledge Technologies},
 series = {i-KNOW '12},
 year = {2012},
 location = {Graz, Austria},
 pages = {26:1--26:8},
 articleno = {26},
 numpages = {8},
 keywords = {interactive visual analysis, microarray data, visual analytics},
 abstract = {Microarray data represents the expression levels of genes for different 
 samples and for different conditions. It has been a central topic in bioinformatics 
 research for a long time already. Researchers try to discover groups of genes that
 are responsible for specific biological processes. Statistical analysis tools and
 visualizations have been widely used in the analysis of microarray data. Researchers 
 try to build hypotheses on both the genes and the samples. Therefore, such analyses 
 require the joint exploration of the genes and the samples. However, current methods 
 in interactive visual analysis fail to provide the necessary mechanisms for this joint 
 analysis. In this paper, we propose an interactive visual analysis framework that 
 enables the dual analysis of the samples and the genes through the use of integrated 
 statistical tools. We introduce a set of specialized views and a detailed analysis 
 procedure to describe the utilization of our framework.},



}