A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

Abstract

Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes.Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics.Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/.Contact: xjzhou@usc.edu; zsh@amss.ac.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Learning regulatory programs by threshold SVD regression

“…Another joint variable and rank selection method (29) uses l 2 group penalty on the rows of C. However, this algorithm along with another recently proposed method (30) can reduce dimension in predictor space but not response space and does not provide information on independent regulatory programs. A sparse network-regularized multiple nonnegative matrix factorization (SNMNMF), which incorporates the known interaction from literature as a prior information in the parameter estimations, was recently proposed for the inference of miRNA-gene regulation (31). However, SNMNMF identifies only the coexistence relationship between predictors and responses without the estimation of the relative strength or direction of regulation.…”

“…Here k · k F is the Frobenius norm. Conditional on U, our estimation procedure with a hardthresholding function is equivalent to an l 0 penalization (31), and hence without the orthogonal constraints, the number of nonzero entries in the final estimate ofV v can be easily shown to be an unbiased estimator of the degrees of freedom of the l 0 penalization. Therefore, we estimate df v by d df v = #ðV!…”

Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network

“…In order to evaluate the performance of MBCFM and fairly compare it with the other two existing methods of Mirsynergy and SNMNMF in miRNA regulatory modules detection, we apply these three methods to the ovarian cancer dataset processed by Zhang et al [10]. The miRNA and mRNA expression profiles for 385 samples were downloaded from TCGA data portal (http://cancergenome.nih.gov/), each measuring 559 miRNAs and 12456 mRNAs, respectively.…”

“…We believe that the mRNAs in R-pair structure have more functional consistency than the mRNAs that are not. In fact, there are several studies showing that miRNA tends to target highly connected mRNAs or proteins in PPI networks, and that the R-pair structure plays important roles in cell function [9][10][11]13]. So, the R-pair structure can be regarded as the core of a CMFM.…”

“…For example, SNMNMF (Sparse Network-regularized Multiple Non-negative Matrix Factorization) proposed by Zhang et al [10] described a factorized matrix framework to identify composite miRNA functional modules by integrating miRNA and gene expression profiles, the protein-protein interactions (PPIs) and transcription factor binding sites. Another method called Mirsynergy, which was proposed by Li et al [11], detected miRNA regulatory modules based on synergistic scores between two miRNAs.…”