High-Dimensional Statistics with R

University of Edinburgh

Online

Instructors:

Helpers:

Overview

High-Dimensional Statistics with R

This course is intended for those who have a working knowledge of statistics and linear models with R and wish to learn high-dimensional statistical methods with R.

This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records). These datasets can be challenging to approach, as they often contain many more features than observations, and it can be difficult to distinguish meaningful patterns from natural underlying variability. To this end, we will introduce and explain a range of methods and approaches to disentangle these patterns from natural variability. After completion of this course, learners will be able to understand, apply, and critically analyse a broad range of statistical methods. In particular, we focus on providing a strong grounding in high-dimensional regression, dimensionality reduction, and clustering.

Ed-DaSH

Ed-DaSH is a Data Science training programme for Health and Biosciences. The team has developed workshops using The Carpentries platform on the following topics. See workshops for dates and registration details. All workshops will be delivered remotely.

General Information

Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

Requirements: Participants must have access to a computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you.

Contact: Please email for more information.

Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.


Code of Conduct

Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.


Schedule

The lesson taught in this workshop is being piloted and a precise schedule is yet to be established.

Day 1 :

Pre-workshop Setup
Lessons
High-Dimensional Statistics with R Introduction
Regression with many features
Break
Morning 10:45 - 11:00

Day 2 :

Lessons
High-Dimensional Statistics with R Regularised regression
Break
Morning 10:45 - 11:00

Day 3 :

Lessons
High-Dimensional Statistics with R Principal component analyses
Factor analysis
Break
Morning 10:45 - 11:00

Day 4 :

Lessons
High-Dimensional Statistics with R K-means clustering
Hierarchical clustering
Break
Morning 10:45 - 11:00

Setup

To participate in a workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Install the videoconferencing client

If you haven't used Zoom before, go to the official website to download and install the Zoom client for your computer.

Set up your workspace

Like other Carpentries workshops, you will be learning by "coding along" with the Instructors. To do this, you will need to have both the window for the tool you will be learning about (a terminal, RStudio, your web browser, etc..) and the window for the Zoom video conference client open. In order to see both at once, we recommend using one of the following set up options:

This blog post includes detailed information on how to set up your screen to follow along during the workshop.

Please check the “Setup” page of the lesson site for instructions to follow to obtain the software and data you will need to follow the lesson.