Jiawei Luo

Identifying lncRNA and mRNA Co-Expression Modules from Matched Expression Data in Ovarian Cancer

Long non-coding RNAs (lncRNAs) have been shown to be involved in multiple biological processes and play critical roles in tumorigenesis. Numerous lncRNAs have been discovered in diverse species, but the functions of most lncRNAs still remain unclear. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. With the advances of high-throughput technologies, the increasing availability of genomic data creates opportunities for deciphering the molecular mechanism and underlying pathogenesis of human diseases. Here, we develop an integrative framework called JONMF to identify lncRNA-mRNA co-expression modules based on the sample-matched lncRNA and mRNA expression profiles. We formulate the module detection task as an optimization problem with joint orthogonal non-negative matrix factorization that could effectively prevent multicollinearity and produce a good modularity interpretation. The constructed lncRNA-mRNA co-expression network and the gene-gene interaction network are used as the network-regularized constraints to improve the module accuracy, while the sparsity constraints are simultaneously utilized to achieve modular sparse solutions. We applied JONMF to human ovarian cancer dataset and the experiment results demonstrate that the proposed method can effectively discover biologically functional co-expression modules, which may provide insights into the function of lncRNAs and molecular mechanism of human diseases.

BiModule: Biclique Modularity Strategy for Identifying Transcription Factor and microRNA Co-Regulatory Modules

Systematic identification of gene regulatory modules can provide invaluable knowledge towards understanding aberrant transcriptional/post-transcriptional collaborative regulatory (co-regulatory) effects in cancer. Transcription factor (TF) and microRNA (miRNA) are known as two classes of prominent regulators that play crucial roles in gene regulation. Existing studies on gene regulatory modules identification mainly focused on the miRNA-mediated regulatory network, and few considered these two regulators in a co-occurring network. In this current study, we developed a computational method called BiModule for systematically identifying TF-miRNA co-regulatory modules. BiModule operates in two main stages: it first constructs a cancer-specific regulator-mRNA network and then identifies modules based on maximal bicliques by employing biclique modularity strategy, which is a novel flexible method for bipartite graph mining. We applied our model to a cervical cancer dataset. The results showed that the TF-miRNA co-regulatory modules identified by BiModule exhibit denser connections and stronger expression correlations than another existing related method. Moreover, the BiModule-modules exhibit high biological functional enrichment. In addition, based on Kaplan-Meier survival analysis, we found a number of modules with significant prognostic associations. Availability: the R source code of BiModule is available at https://github.com/chupan1218/BiModule.