Gene expression network reconstruction by LEP method using microarray data.
Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.
School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China.
Na
N
You
Peng
P
Mou
Ting
T
Qiu
Qiang
Q
Kou
Huaijin
H
Zhu
Yuexi
Y
Chen
Xueqin
X
Wang
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D015203:Reproducibility of Results