The Library
A temporal precedence based clustering method for gene expression microarray data
Tools
Krishna, Ritesh V., Li, Chang-Tsun and Buchanan-Wollaston, Vicky (2010) A temporal precedence based clustering method for gene expression microarray data. BMC Bioinformatics, Vol.11 . Article 68. doi:10.1186/1471-2105-11-68 ISSN 1471-2105.
PDF
WRAP_Buchanon_wollaston_Temporal_precedence.pdf - Requires a PDF viewer. Download (6Mb) |
Official URL: http://dx.doi.org/10.1186/1471-2105-11-68
Abstract
Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.
Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system.
Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QK Botany Q Science > QH Natural history > QH426 Genetics |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) > Warwick HRI (2004-2010) |
||||
Library of Congress Subject Headings (LCSH): | Gene expression -- Research, DNA microarrays -- Research, Bioinformatics, Arabidopsis thaliana -- Genetics | ||||
Journal or Publication Title: | BMC Bioinformatics | ||||
Publisher: | BioMed Central Ltd. | ||||
ISSN: | 1471-2105 | ||||
Official Date: | 30 January 2010 | ||||
Dates: |
|
||||
Volume: | Vol.11 | ||||
Page Range: | Article 68 | ||||
DOI: | 10.1186/1471-2105-11-68 | ||||
Status: | Peer Reviewed | ||||
Access rights to Published version: | Open Access (Creative Commons) |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year