A standard bioinformatics analysis of ’omics data will produce a list of molecules following statistical analysis. In the context of transcriptomics, these molecules are genes or transcripts and the statistical approach used to identify them is mostly a differential expression analysis. Once genes have been identified as differentially expressed in an experiment, biologists are often interested in understanding their biological implications. This is done by understanding their functional role in the biological system being investigated. The role and function of many genes is known to some extent and this is an area of continued research. Knowledge on gene function is often encoded into knowledgebases such as the gene ontology and other pathway databases. Given these functional annotations, we are interested in identifying over-represented functions in our data.
To do so, we use gene-set enrichment analysis, a group of methods designed to identify enriched functions represented by collections of genes known as gene-sets. These approaches often identify 100s-1000s of gene-sets/pathways that then need to be curated manually. To automate the process of condensing this knowledge, we developed vissE a tool to summarise, interpret, and visualise higher-order pathways/processes. It then provides a suite of modules to assess the functional roles in each higher-order pathway, thus providing biologists with a holistic view of the biological system they are investigating.
This workshop will focus on performing gene-set enrichment analysis of transcriptomic data and visualising the results of enrichment analysis. The workshop will explore the biology related to the epithelial to mesenchymal transition (EMT). We will perform single-sample gene-set enrichment using methods in the singscore package to explore molecular phenotypes in individual samples. Following this, we will perform gene-set enrichment analysis using tools from the limma and edgeR packages. Finally, we will demonstrate a graph-based approach to visualise, summarise, and interpret results of gene-set enrichment analysis.
The workshop will be organised into two broad sections:
Detailed material can be found: https://davislaboratory.github.io/GenesetAnalysisWorkflow/articles/workflow_singscore_vissE.html.
Keywords: Pathways, Network analysis, RNA-seq, Gene-set enrichment, single-sample phenotyping, molecular phenotypes, EMT
Requirements: Internet enabled laptop. It will be assumed that participants have some programming experience in R.
Relevance: This workshop is designed for students and researchers interested in gaining a mechanistic understanding of transcriptomics experiments using pathway analysis tools. It will be relevant to those who wish to interrogate their data using network analysis and visualisation approaches.
Postdoctoral fellow, WEHI
Dr. Dharmesh D. Bhuva is a computational systems biologist in the molecular analysis of cancer transcriptomic data. He was awarded his PhD in 2020 at the University of Melbourne following which he started his post-doctoral research at the Davis laboratory at the Walter and Eliza Hall Institute. His PhD focused on the development of novel methods to model and dissect regulatory mechanisms underlying the complex and heterogeneous molecular phenotypes of cancers. His expertise lies in the inference of regulatory mechanisms in cancers, modelling of regulation, network biology, and molecular phenotype classification, and the development of novel methods. He has experience in analysing multiple data types including proteomic, transcriptomic, and epigenetic data. Dr. Bhuva has applied his expertise in commercially funded projects led by the CRC for Cancer Therapeutics.
Senior Postdoctoral fellow, WEHI
Dr. Chin Wee Tan is a senior research fellow at the Walter and Eliza Hall Institute of Medical Research (WEHI). Dr Tan competed his PhD in Biomedical Engineering (Computational Systems Biology) from the University of Melbourne in 2011. Dr Tan’s early research has revolved around systems biology of colon cancer, utilizing state-of the art imaging technologies, quantitative microscopy, and mathematical modelling approaches to advance the knowledge and treatment of colon cancer. At WEHI Systems Biology Division, Dr Tan led a systems biology team spearheading computational cancer biology research studying colon cancer biology, using 3D quantitative microscopy techniques, computational systems modelling and image analytics of cell-lines and colon organoids “mini-gut” cultures. These efforts resulted in the development of a platform for image-based colon cancer organoid screening for deploying in any clinical laboratory. Dr Tan joined A/Prof Melissa Davis’s laboratory in 2019, focusing his efforts on computational cancer biology. He has since worked on bioinformatics/computational cancer-related (and now covid-19) projects as well as commercial drug discovery projects involving analyses of multi-omic data associated with responses to drugs in different cancer models. Since 2020, Dr Tan has been leading efforts in developing bioinformatics pipelines for the spatial transcriptomics analyses for lung and myo-cardial COVID-19/ SARS-CoV-2 infections and Leukemia, in collaboration with colleagues at QUT, UQ and Unimelb. In 2021, Dr Tan was awarded the Regional Digital Collaboration Grants from the Australian Academy of Science and Department of Industry, Science, Energy and Resources for COVID-19 projects.