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ICSA2021
Welcome to the wiki of the ICSA 2021 half-day Short Course!
organized by GW Computational Biology Institute & Merck
Participants 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 exercises with real data. Participants are strongly encouraged to bring their own data and study examples for application. Open to computational biologists, bioinformaticians, principal investigators, and their research teams including advanced Ph.D. students. Basic familiarity with multi-omics upstream bioinformatics tools are recommended. Beginner-level familiarity with R is required. 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, which could result in incorrect interpretations. Further, there are also a large collection of downstream analysis software platforms and appropriately selecting the best tool can be extraneous for untrained researchers.
In this workshop, we will thus present a high-level introduction to computational multi-omics, highlighting the state-of-the-art in the field as well as outstanding challenges geared towards downstream analysis methods. This will include an introduction to the biological goals of typical multi-omics studies and the statistical methods currently available to achieve them.
We will begin with an overview of the statistical challenges inherent to analyzing the compositional data arising in multi-omics studies. Introductory lectures will include: The challenges associated with precisely testing for multivariable association testing in population-scale meta-omics studies Challenges and advances in pathway enrichment analyses including techniques and characterization of omics features Meta-analysis of multiple datasets for high-sensitivity discovery and integration with other types of data such as electronic health records and imaging 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:
- Tweedieverse tutorial and Tweedieverse examples: A unified statistical framework for differential analysis of multi-omics data
- deepath: 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
This workshop will be run by a joint effort between George Washington University and Merck Research Laboratories. Researchers from both industry and academia will come together to share a diverse perspective on the topic both from drug discovery and basic science angles, enabling attendees to achieve a holistic view of multi-omics and clinical data integration through the use of state-of-the-art tools applied to motivating examples and use cases.
preparation tasks are optional however they help the organizers to focus on scientific discussion rather than troubleshooting technical issues.
- Install the latest R and Rstudio on your local computer
- Install listed software in the learning objectives
- Try to run demos of each software
- Bring your data to apply these techniques
- For windows OS please use Command Prompt with admin access
Time: Event
1:30 pm: Welcome and introduction to multi-omics
1:50 pm: Multivariable association testing: challenges and techniques
2:20 pm: Tweedieverse tutorial and applications exercises
3:00 pm: 30 minutes break
3:30 pm: deepath for pathway enrichment analysis
3:45 pm: deepath totrial
4:00 pm: Omics community detection using omeClust
4:20 pm: IntegratedLearner for multi-omics prediction and classification
4:40 pm: Q/A and Wrap-up, Tips for visualization of results
Ali Rahnavard: George Washington University (Instructor and Organizer)
Himel Mallick: Merck Research Laboratories (Instructor)
This material is based upon work supported by the National Science Foundation under Grant Number (2028280 and 2109688), Bill & Melinda Gates Foundation under Investment number (016930).