Recursive regularization for inferring gene networks from time-course gene expression profiles.
BACKGROUND
Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.


RESULTS
By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.


CONCLUSIONS
The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.
Shimamura
Teppei
T
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. shima@ims.u-tokyo.ac.jp
Imoto
Seiya
S
Yamaguchi
Rui
R
Fujita
André
A
Nagasaki
Masao
M
Miyano
Satoru
S
D000465:Algorithms; D045744:Cell Line, Tumor; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D020890:Neuregulin-1 / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D013997:Time Factors
D020890:Neuregulin-1