Description
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.
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.
