Date: Wednesday 18 April 2018, 16:00 – 17:00 GMT
Statistical analysis offers powerful approaches to mine complex and high-dimension datasets, find significant features, and build prediction models with high-prediction performance. Due to the high number of methods available, their complex mathematical background, and the potential pitfalls due to biases and overfitting, a good understanding of each step as well as an environment allowing to efficiently manage the whole workflow, are of major importance.
In his webinar on Statistical workflows, Etienne Thévenot, CEA, France, will show how to build a statistical workflow within the user-friendly Galaxy environment, including: normalization (signal drift and batch effect), quality control, univariate hypothesis testing, multivariate modelling with (Orthogonal) Partial Least Squares, and feature selection (with Partial Least Squares, Random Forest and Support Vector Machines). The example dataset used is (MTBLS404), the Sacurine study, that aims at discovering physiological variations of the human urine metabolome with age, body mass index, and gender. Etienne will also see how data from the MetaboLights repository can be uploaded directly into Galaxy workflows.
Additional statistical modules and public analyses are available on the Workflow4Metabolomics platform.
For registration, click here (Registration closed)