Who should attend:
Researchers and scientists who want to model Omics data (Metabo(n/l)omics, Proteomics, Genomics, etc. with data from LC-MS, GC-MS or NMR spectrum, Gel Electrophoresis, Fluorescence measurements, Gene Chip Arrays, etc.) from all sectors of biological/pharmaceutical/medical/chemical/F&B/ with a lot of process understanding but with no or limited statistical background..
Info:
Using the latest multivariate techniques, participants will learn how to build multivariate prediction models, from reading the data until defining the model for a real-life application. The course is composed of lectures, demonstrations, and computer exercises in software SIMCA® /SIMCA® Omics Skin based on real-life datasets.
<pargraph>Course Objectives:</paragraph>
To guide the attendees through their journey from importing the omics dataset to a multivariate biomarker identification based on their omics data.
Topics include:
- Organization, visualization, and treatment of different types of data.
- Principal components analysis (PCA) for overview of tables, finding outliers, groups, and trends in data.
- Orthogonal Projections to Latent Structures - Discriminant Analysis (OPLS-DA) for class discrimination and biomarker identification.
- Multiblock Orthogonal Component Analysis (MOCA) for data integration and fusion in system biology.
SCHEDULE
Session 1 (3.5h): Introduction to MVDA
- Introduction to Sartorius Digital Solutions.
- Introduction to omics data.
- Data analysis objectives.
- Workflow in omics.
- Introduction to Principal Component Analysis (PCA) for overview of data tables.
- Software click-along demo.
Session 2 (3.5h): OPLS discriminant analysis (OPLS-DA), Two-group problem, Visualization.
- Introduction to discriminant analysis, focus on two-group problem.
- Introduction to Omics skin and its Analysis wizard.
- How the Analysis wizard works, the S-plot.
- Plots after leaving Analysis wizard.
- Additional useful plots for visualization.
- Software click-along demo.
Session 3 (3.5h): More OPLS-DA Theory, Three-group problem.
- How to handle the three-group problem.
- How to handle multi-group investigations.
- Identifying shared and unique variation,the SUS-plot.
- More on OPLS-DA TheorySoftware click-along demo.
Session 4 (3.5h): Advanced Omics Applications
- Analysis of Omics Data With Few Samples.
- Ensuring quality data.
- Analyzing Group vs Group Data.
- Analyzing Difference data.
- Group to Group vs Difference Data.
- A Multivariate Approach to Systems Biology.
- Software click-along demo.
- Course debriefing and final Q&A.
