CE_24C: Orchestrating biomarker discovery and pathway enrichment using multi-omics integration and data science (Added Fee)

Methodological advancements paired with measured multi-omics data using high-throughput technologies enable capturing comprehensive snapshots of biological activities. In particular, low-cost, culture-independent omics profiling has made metagenomics, metabolomics, and proteomics ('multi-omics') surveys of human health, other hosts, and the environment. The resulting data have stimulated the development of new statistical and computational approaches to analyze and integrate omics data, including human gene expression, microbial gene products, metabolites, and proteins, among others. Multi-omics data generated from diverse platforms are often fed into generic downstream analysis software without proper appreciation of the inherent data differences, resulting in incorrect interpretations. Further, there are also an extensive collection of downstream analysis software platforms, and appropriately selecting the best tool can be extraneous for untrained researchers. This workshop will thus present a high-level introduction to computational multi-omics, highlighting the state-of-the-art in the field and outstanding challenges geared towards downstream analysis methods. The workshop will include introducing typical multi-omics studies' biological goals and the statistical methods currently available to achieve them. The workshop is project-focused and uses a hands-on approach. Participants are encouraged to attend with a specific study or project in mind for the content to be applied in the short term. The workshop will use real data for the exercises. Workshop attendees will gain hands-on experience with these analyses using tools for pattern discovery in multi-omics. Interspersed with lecture content, attendees will work through multi-omics analysis tutorials. Tools will include: omeClust: Omics community detection using multi-resolution clustering Tweedieverse: A unified statistical framework for differential analysis of multi-omics deepath: omics pathway enrichment analysis IntegratedLearner: Integrated machine learning for multi-omics prediction and classification to stratify patients for therapeutic intervention Publication-quality figure generation and effective visualization of the results