The Library
Listen to genes : dealing with microarray data in the frequency domain
Tools
Feng, Jianfeng, Yi, Dongyun, Krishna, Ritesh, Guo, Shuixia and Buchanan-Wollaston, Vicky (2009) Listen to genes : dealing with microarray data in the frequency domain. PL o S One, Vol.4 (No.4). doi:10.1371/journal.pone.0005098 ISSN 1932-6203.
|
PDF
WRAP_Feng_Listen_to_genes.pdf - Requires a PDF viewer. Download (1325Kb) |
Official URL: http://dx.doi.org/10.1371/journal.pone.0005098
Abstract
Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes
normalization, clustering and network analysis of genes.
Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and
estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The
normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger
causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger
causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting
networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000
genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new
global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.
Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum
analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency
domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray
data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by
step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of
potential interest to Arabidopsis researchers.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics Q Science > QK Botany |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing 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): | Genes -- Mathematical models, DNA microarrays -- Mathematical models, Arabidopsis -- Mathematical models | ||||
Journal or Publication Title: | PL o S One | ||||
Publisher: | Public Library of Science | ||||
ISSN: | 1932-6203 | ||||
Official Date: | 6 April 2009 | ||||
Dates: |
|
||||
Volume: | Vol.4 | ||||
Number: | No.4 | ||||
DOI: | 10.1371/journal.pone.0005098 | ||||
Status: | Peer Reviewed | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Funder: | University of Warwick. Dept. of Computer Science, European Union (EU), Engineering and Physical Sciences Research Council (EPSRC), Research Councils UK (RCUK) |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year