A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

Integrating gene regulatory pathways into differential network analysis of gene expression data

The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.