Reconstruction of Transcription Regulatory Networks by Stability-Based Network Component Analysis

Abstract

Reliable inference of transcription regulatory networks is still a challenging task in the field of computational biology. Network component analysis (NCA) has become a powerful scheme to uncover the networks behind complex biological processes, especially when gene expression data is integrated with binding motif information. However, the performance of NCA is impaired by the high rate of false connections in binding motif information and the high level of noise in gene expression data. Moreover, in real applications such as cancer research, the performance of NCA in simultaneously analyzing multiple candidate transcription factors (TFs) is further limited by the small sample number of gene expression data. In this paper, we propose a novel scheme, stabilitybased NCA, to overcome the above-mentioned problems by addressing the inconsistency between gene expression data and motif binding information (i.e., prior network knowledge). This method introduces small perturbations on prior network knowledge and utilizes the variation of estimated TF activities to reflect the stability of TF activities. Such a scheme is less limited by the sample size and especially capable to identify condition-specific TFs and their target genes. Experiment results on both simulation data and real breast cancer data demonstrate the efficiency and robustness of the proposed method.

Reconstruction of Gene Regulatory Network from Gene Perturbation Data Current Methods and Problems

“…Since the nature of GRNs that consists of simultaneous observation and analysis of more than one outcome variable [13], multiple regression analysis wise choice to reconstruct GRNs. There are a number of methods in this category, such as Multiple Linear Regression [27], Principle Component Regression [28], Partial Least Squares [29], Least Absolute Shrinkage [1] and Selection Operator (LASSO) [30] and Canonical Correlation Analysis [31]. While the linear regression model consists of a deterministic part and a random part, generally defined as (1) The deterministic portion of the model, (2) defines as, for any value of the independent variable, , the population mean of the dependent or response variable, , is described by the straight-line function .…”