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Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks
July-September 2008 (vol. 5 no. 3)
pp. 401-415
ASCII Text
x 
 
Peng Wei, Wei Pan, "Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 5, no. 3, pp. 401-415, July-September, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2007.1062,
author = {Peng Wei and Wei Pan},
title = {Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {5},
number = {3},
issn = {1545-5963},
year = {2008},
pages = {401-415},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2007.1062},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks
IS - 3
SN - 1545-5963
SP401
EP415
EPD - 401-415
A1 - Peng Wei,
A1 - Wei Pan,
PY - 2008
KW - LASSO
KW - Microarray
KW - Shrinkage estimator
KW - Stratified analysis
KW - Transcription factor
VL - 5
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Useful information on transcriptional networks has been extracted by regression analyses of gene expression data and DNA-protein binding data. However, a potential limitation of these approaches is their assumption on the common and constant activity level of a transcription factor (TF) on all the genes in any given experimental condition; for example, any TF is assumed to be either an activator or a repressor, but not both, while it is known that some TFs can be dual regulators. Rather than assuming a common linear regression model for all the genes, we propose using separate regression models for various gene groups; the genes can be grouped based on their functions or some clustering results. Furthermore, to take advantage of the hierarchical structure of many existing gene function annotation systems, such as Gene Ontology (GO), we propose a shrinkage method that borrows information from relevant gene groups. Applications to a yeast dataset and simulations lend support for our proposed methods. In particular, we find that the shrinkage method consistently works well under various scenarios. We recommend the use of the shrinkage method as a useful alternative to the existing methods.

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Index Terms:
LASSO, Microarray, Shrinkage estimator, Stratified analysis, Transcription factor
Citation:
Peng Wei, Wei Pan, "Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 5, no. 3, pp. 401-415, July-Sept. 2008, doi:10.1109/TCBB.2007.1062
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