Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network

In recent years, due to the high availability of omic data, data-driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omics approaches have been proposed in the literature, none of which takes into consideration the intrinsic topology of each omic, though. In this work, an unsupervised learning method based on a deep neural network is proposed. Foreach omic, a separate network is trained, whose outputs are fused into a single graph; at this purpose, an innovative loss function has been designed to better represent the data cluster manifolds. The graph adjacency matrix is exploited to determine similarities among samples. With this approach, omics having a different number of features are merged into a unique representation. Quantitative and qualitative analyses show that the proposed method has comparable results to the state of the art. The method has great intrinsic flexibility as it can be customized according to the complexity of the tasks and it has a lot of room for future improvements compared to more fine-tuned methods, opening the way for future research

Do count-based differential expression methods perform poorly when genes are expressed in only one condition?

A response to 'Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data' by Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND and Betel D in Genome Biology, 2013, 14:R95

Discovering Yersinia-Host Interactions by Tissue Dual RNA-Seq.

A detailed knowledge about virulence-relevant genes, as well as where and when they are expressed during the course of an infection is required to obtain a comprehensive understanding of the complex host-pathogen interactions. The development of unbiased probe-independent RNA sequencing (RNA-seq) approaches has dramatically changed transcriptomics. It allows simultaneous monitoring of genome-wide, infection-linked transcriptional alterations of the host tissue and colonizing pathogens. Here, we provide a detailed protocol for the preparation and analysis of lymphatic tissue infected with the mainly extracellularly growing pathogen Yersinia pseudotuberculosis. This method can be used as a powerful tool for the discovery of Yersinia-induced host responses, colonization and persistence strategies of the pathogen, and underlying regulatory processes. Furthermore, we describe computational methods with which we analyzed obtained datasets