Statistical methods for big data in life sciences and health with R

Overview

In this course, biologists, computational biologists and bioinformatics scientists will acquire the key competencies that they need when dealing with big data from the health and life sciences fields. We will discuss the visualisation of large data sets, machine-leaning algorithms, linear models, classification algorithms as well introduce neural networks. Since many R functions cannot handle large datasets, we will also explore alternative solutions for several widely used statistical functions.

Audience

Biologists and bioinformaticians who need to handle large datasets, in particular those that R standard functions cannot deal with. The participants must already be confortable with R (i.e use linear models, calculate a correlation, and visualization of data using R).

Learning objectives

At the end of the course, the participants will be able to:

1. Identify the challenges behind the analysis of big datasets
2. Explore big datasets using visualisation and classification tools
3. Apply decisional machine learning and clustering methods on large datasets
4. Apply linear models functions on big data
5. Classify big datasets using neural network methods

Schedule

(subject to changes)

The course will cover the following topics:

Day 1.

• Overview: big data case studies in health domain
• Identify the general challenges behind big data analysis (model and overfitting)
• Big data visualisation

Day 2. Linear models

Day 3.

• Big data exploration and classifications
• Machine learning and decisional algorithms – unsupervised learning

Day 4. Introduction to: Decision Tree, Random forest, Neural Networks, Deep learning

Prerequisites

Knowledge / Competencies

• Statistics beginner level (T-test, multiple testing methods, linear models)
• R beginner level (Rstudio, ggplot, plots, install a library, matrix manipulation, read files)

Technical

• You are required to bring your own laptop, with a working Wifi connection, and the latest versions of R and RStudio installed. Furthermore, you will asked to install various packages.

Application

The course if full, therefore closed.

The registration fees for academics are 240 CHF for academics and 1200 CHF for for-profit companies. This includes course content material, coffee breaks and social dinner on Tuesday 4 June (Save the date, more info to follow).

Deadline for registration and free-of-charge cancellation is set is set to 25/05/2018. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation.

Venue and Time

University of Lausanne, (Metro M1 line, Sorge station), Amphipôle Building, classroom 202.

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants on due time.

Additional information

Coordination: Diana Marek, Grégoire Rossier

We will recommend 1 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

For more information, please contact training@sib.swiss.