Utilizing Embedding Strategies and Gene Co-Expression Networks for Comparative Transcriptomic Analyses

Abstract: The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We ultimately developed a methodology packaged as a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. We demonstrated the capabilities of our proposed method for comparing GCNs, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species.

Biography: Katie Ovens is a Ph.D. candidate in the Computer Science Department at the University of Saskatchewan under the supervision of Dr. Ian McQuillan and Dr. Brian Eames. Her doctoral research has involved the bioinformatic analysis and visualization of transcriptomic datasets. She is interested in biological network analysis, with her thesis paying particular attention to gene co-expression networks and their comparison across various species to make evolutionary inferences. During both her M.Sc. and Ph.D., she has also collaborated on many multi-disciplinary projects, including the evaluation of computational methods to make inferences from biomedical datasets and medical imaging analysis using machine learning and deep learning approaches.

Thursday November 5, 2020 at 2:00 PM via Zoom

Link: https://zoom.us/j/98462644378?pwd=S2ZuQTVZTzB5Vmh6VEozemNJS0NOZz09

Meeting ID: 984 6264 4378