Statistical Analysis of Biological Data in R

Wednesday 15th March, 13:00 - 17:00
Pam Liversidge Building - Design Studio 2 - E06, or ONLINE
Registration Link

Overview

This course provides a refresher on the foundations of statistical analysis. The course is aimed at scientists at all levels – especially those whose formal education likely included statistics, but who have not perhaps put this into practice since. The focus of the course is on understanding the principles behind statistical testing, how to choose and execute the most appropriate test for your data, and how to interpret the result.

Note that you will need to be familiar with R to access this course. Please see our latest introductory course on using R to check if you have sufficient background https://sbc.shef.ac.uk/training/r-introduction-2023-02-29/

Who should attend this course?

The course is aimed primarily at scientists at all levels – especially those whose formal education likely included statistics, but who have not perhaps put this into practice since.

Objectives:- After this course you should be able to:

State the assumptions required for a one-sample and two-sample t-test and be able to interpret the results of such a test
Know when to apply a paired or independent two-sample t-test
To perform simple statistical calculations using R
Understand the limitations of the tests taught within the course
Know when more complex statistical methods are required

Aims:- During this course you will learn about:

Different types of data, distributions and structure within data
Summary statistics for continuous and discrete data
Formulating a null hypothesis
Assumptions of one-sample and two-sample t-tests
Interpreting the result of a statistical test
Statistical tests of categorical variables (Chi-squared and Fisher’s exact tests)
Non-parametric versions of one- and two-sample tests (Wilcoxon tests)

Prerequisites

Introductory-level R; for example attending our Data Manipulation and Visualisation course

Provisional Timetable and Materials

Setup

1) First, install both R and RStudio for your operating system.

Windows

Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select “Run as administrator” instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.

Mac

Install R by downloading and running this .pkg file from CRAN. Also, please install the free RStudio IDE

Linux

You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install free the RStudio IDE.

2) Type the following into the R console to install some extra R packages required for the workshop

install.packages("dplyr")
install.packages("ggplot2")
install.packages("readr")
install.packages("rmarkdown")
install.packages("tidyr")

Mac Users may get the following error message when trying to install these packages

xcrun error: inactive developer path (/Library/Developer/CommandLineTools), missing xcrun at:.....

If this is the case, you will need to follow the instructions from this link to install “Xcode”

https://apple.stackexchange.com/questions/254380/why-am-i-getting-an-invalid-active-developer-path-when-attempting-to-use-git-a

Window users might get a message that Rtools is required. This shouldn’t be necessary, but you might need it for other packages. It can be installed here:-

https://cran.r-project.org/bin/windows/Rtools/

Instructors

Mark Dunning, Bioinformatics Core Director
Emily Chambers, Bioinformatics Core Scientist
Aya Elwazir, PhD Student