Orchestrating Biomarker Discovery and Pathway Enrichment using Multi-omics Integration and Data Science

This repo contains the materials for the short course Orchestrating Biomarker Discovery and Pathway Enrichment using Multi-omics Integration and Data Science at 2023 Joint Statistical Meetings (JSM), Toronto, ON, Canada on August 8, 2023.

Abstract

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 is 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 that will be covered include:

• Tweedieverse tutorial and Tweedieverse examples: A unified statistical framework for differential analysis of multi-omics data
• omePath: omics pathway enrichment analysis
• omeClust: Omics community detection using multi-resolution clustering
• 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

Instructors

• Ali Rahnavard, George Washington University

• Himel Mallick, Cornell University

Agenda

8:30 AM Welcome and introduction to multi-omics

8:45 AM Multivariable association testing: challenges and techniques

9:15 AM Tweedieverse tutorial

10:00 AM Q&A and exercises

10:15 AM Mid-morning Break

10:30 AM Introduction to pathway enrichment analysis

11:00 AM omePath for pathway enrichment analysis

11:30 AM omePath tutorial

12:15 PM Q&A and exercises

12:30 PM Lunch break

2:00 PM Omics community detection using omeClust

2:30 PM omeClust tutorial

3:15 PM Mid-afternoon break

3:30 PM Introduction to multiview analysis

3:45 PM IntegratedLearner for multi-omics prediction and classification

4:00 PM IntegratedLearner tutorial

4:30 PM Miscellaneous topic in omics data science

4:45 PM Q&A and Wrap-up, Tips for visualization of results

Prepration

Preparation tasks are optional. However, they help the organizers focus on scientific discussion rather than troubleshooting technical issues.

• Install the latest R and RStudio on your local computer
• Install the listed software in the Abstract
• Try to run demos of each software
• Bring your data to apply these techniques
• Join workshop Slack (link provided in workshop outline)