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

Zhang S, Li Q, Liu J, Zhou XJ - Bioinformatics (2011)

Bottom Line: The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm.We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated.Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics.

View Article: PubMed Central - PubMed

Affiliation: Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn

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.cn

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Related in: MedlinePlus

About 44.4% of the miRNAs in identified comodules have previously been reported to be cancer related (hypergeometric test, P=1.1×10−6). Of these, 21 miRNAs were specifically related to ovarian cancers (hypergeometric test, P=7.2×10−6).
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Figure 2: About 44.4% of the miRNAs in identified comodules have previously been reported to be cancer related (hypergeometric test, P=1.1×10−6). Of these, 21 miRNAs were specifically related to ovarian cancers (hypergeometric test, P=7.2×10−6).

Mentions: Since our input data included the miRNA and gene expression profiles of ovarian cancer samples, we expect the identified comodules to be related to cancer. To verify this, we used a cancer miRNA benchmark dataset of 147 miRNAs from a review article (Koturbash et al., 2010). Each of these miRNAs was reported in the literature to be dysregulated in one or more cancers. Among these, 41 are relevant to ovarian cancer. Note that this dataset does not include any information from the TCGA ovarian cancer data. Our comodules involve 117 different miRNAs, 52 of which belong to the benchmark set of cancer miRNAs. This ratio is highly significant (P=1.1×10−6) (Figure 2). Even more importantly, 21 of the 52 miRNAs shared by our results and the benchmark are related to ovarian cancer, with an enrichment significance of P=7.2×10−6.Fig. 2.


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

Zhang S, Li Q, Liu J, Zhou XJ - Bioinformatics (2011)

About 44.4% of the miRNAs in identified comodules have previously been reported to be cancer related (hypergeometric test, P=1.1×10−6). Of these, 21 miRNAs were specifically related to ovarian cancers (hypergeometric test, P=7.2×10−6).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117336&req=5

Figure 2: About 44.4% of the miRNAs in identified comodules have previously been reported to be cancer related (hypergeometric test, P=1.1×10−6). Of these, 21 miRNAs were specifically related to ovarian cancers (hypergeometric test, P=7.2×10−6).
Mentions: Since our input data included the miRNA and gene expression profiles of ovarian cancer samples, we expect the identified comodules to be related to cancer. To verify this, we used a cancer miRNA benchmark dataset of 147 miRNAs from a review article (Koturbash et al., 2010). Each of these miRNAs was reported in the literature to be dysregulated in one or more cancers. Among these, 41 are relevant to ovarian cancer. Note that this dataset does not include any information from the TCGA ovarian cancer data. Our comodules involve 117 different miRNAs, 52 of which belong to the benchmark set of cancer miRNAs. This ratio is highly significant (P=1.1×10−6) (Figure 2). Even more importantly, 21 of the 52 miRNAs shared by our results and the benchmark are related to ovarian cancer, with an enrichment significance of P=7.2×10−6.Fig. 2.

Bottom Line: The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm.We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated.Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics.

View Article: PubMed Central - PubMed

Affiliation: Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. zsh@amss.ac.cn

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.cn

Show MeSH
Related in: MedlinePlus