Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data.

Gene coexpression networks (GCNs) are useful tools for inferring gene functions and understanding biological processes when properly constructed. Traditional microarray analysis is being more frequently replaced by bulk-based RNA-sequencing as a method for quantifying gene expression. This new technology requires improved statistical methods for generating GCNs. This chapter explores several popular methods for constructing GCNs using bulk-based RNA-Seq data, such as distribution-based methods and normalization techniques, implemented using the statistical programming language R.

Rab11b-mediated integrin recycling promotes brain metastatic adaptation and outgrowth.

Breast cancer brain metastases (BCBM) have a 5-20 year latency and account for 30% of mortality; however, mechanisms governing adaptation to the brain microenvironment remain poorly defined. We combine time-course RNA-sequencing of BCBM development with a Drosophila melanogaster genetic screen, and identify Rab11b as a functional mediator of metastatic adaptation. Proteomic analysis reveals that Rab11b controls the cell surface proteome, recycling proteins required for successful interaction with the microenvironment, including integrin ß1. Rab11b-mediated control of integrin ß1 surface expression allows efficient engagement with the brain ECM, activating mechanotransduction signaling to promote survival. Lipophilic statins prevent membrane association and activity of Rab11b, and we provide proof-of principle that these drugs prevent breast cancer adaptation to the brain microenvironment. Our results identify Rab11b-mediated recycling of integrin ß1 as regulating BCBM, and suggest that the recycleome, recycling-based control of the cell surface proteome, is a previously unknown driver of metastatic adaptation and outgrowth.

Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data.

Single-cell RNA-Sequencing is a pioneering extension of bulk-based RNA-Sequencing technology. The "guilt-by-association" heuristic has led to the use of gene co-expression networks to identify genes that are believed to be associated with a common cellular function. Many methods that were developed for bulk-based RNA-Sequencing data can continue to be applied to single-cell data, and several of the most widely used methods are explored. Several methods for leveraging the novel time information contained in single-cell data when constructing gene co-expression networks, which allows for the incorporation of directed associations, are also discussed.

DiPhiSeq: robust comparison of expression levels on RNA-Seq data with large sample sizes.

MOTIVATION: In the analysis of RNA-Seq data, detecting differentially expressed (DE) genes has been a hot research area in recent years and many methods have been proposed. DE genes show different average expression levels in different sample groups, and thus can be important biological markers. While generally very successful, these methods need to be further tailored and improved for cancerous data, which often features quite diverse expression in the samples from the cancer group, and this diversity is much larger than that in the control group. RESULTS: We propose a statistical method that can detect not only genes that show different average expressions, but also genes that show different diversities of expressions in different groups. These 'differentially dispersed' genes can be important clinical markers. Our method uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Simulations and real data analysis demonstrate that DiPhiSeq outperforms existing methods in the presence of outliers, and identifies unique sets of genes. AVAILABILITY AND IMPLEMENTATION: DiPhiSeq is publicly available as an R package on CRAN: https://cran.r-project.org/package=DiPhiSeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.