Open Access
Translator Disclaimer
June 2010 Sparse regulatory networks
Gareth M. James, Chiara Sabatti, Nengfeng Zhou, Ji Zhu
Ann. Appl. Stat. 4(2): 663-686 (June 2010). DOI: 10.1214/10-AOAS350

Abstract

In many organisms the expression levels of each gene are controlled by the activation levels of known “Transcription Factors” (TF). A problem of considerable interest is that of estimating the “Transcription Regulation Networks” (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L1 penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates.

Citation

Download Citation

Gareth M. James. Chiara Sabatti. Nengfeng Zhou. Ji Zhu. "Sparse regulatory networks." Ann. Appl. Stat. 4 (2) 663 - 686, June 2010. https://doi.org/10.1214/10-AOAS350

Information

Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62116
MathSciNet: MR2758644
Digital Object Identifier: 10.1214/10-AOAS350

Keywords: E. coli , L_1 penalty , sparse network , Transcription regulation networks

Rights: Copyright © 2010 Institute of Mathematical Statistics

JOURNAL ARTICLE
24 PAGES


SHARE
Vol.4 • No. 2 • June 2010
Back to Top