Abstract
In the past two decades, the biologists are
able to identify gene signatures associated with the phenotypes through
monitoring gene expressions using high-throughput biotechnologies. The gene
signatures have been successfully applied to drug development, disease
prevention, crop improvement, etc. However, ignoring the interactions among
genes has weaken the prediction power of gene signatures in real applications.
The gene regulatory network, in which genes are present by nodes and the
associations between genes are present by edges, are typically constructed to
analyze and visualize the gene interactions. Particularly, we proposed to
measure the strength of (direct or indirect) associations by the coefficient of
intrinsic dependence (CID) to capture possible nonlinear gene relationships.
While encountering pathways analysis in a larger scale, the stepwise gene
(variable) selection may help to identify relevant genes with correct order
from upstream to downstream in a pathway. In this study, we propose to perform
the stepwise pathway analysis on microarray expression data using the CID along
with the partial coefficient of intrinsic dependence (pCID). The proposed
method aims to reduce the high false-positive rates using the CID along in
stepwise variable selections. The method was examined using the simulated
networks, and the well known CBF-COR pathway under cold stress using
Arabidopsis microarray data. It was also practiced on construction of bHLH gene
regulatory pathways under abiotic stresses using rice microarray data. The
proposed method can efficiently decipher the gene regulatory pathways and
achieve higher prediction power in real applications.