Analysing biological data by model fitting in GraphPad Prism
Monday, 06 December 2021, 9.30am to 12pm
Apply for this courseThe course is suitable for all MSD postgraduate research students and early career researchers wanting to analyse and compare their biological data using model fitting. No prior statistical knowledge is required.
COURSE AIM
Experimental biologists often need to compare the outcome of multiple treatments when data is collected over multiple doses or time points. This course explores how to statistically compare multiple treatment curves directly, without the need for repeated measurements, and highlight the utility of the procedure in revealing mechanistic insights.
No prior knowledge of mathematical modelling or statistics will be assumed and discussion will be restricted to tools available in Graphpad Prism.
The course has been adapted to be delivered remotely.
It includes a 2.5 hour online demonstration, offline exercises to become familiar with GraphPad Prism and workshop content, and an online optional workshop to answer questions and obtain assistance with your own data (all online).
COURSE DESCRIPTION
The course contains the following sections:
Online demonstration (2.5 hours):
- Examples of biological data where model fitting is useful.
- How to fit biological data to models (including how to assess the quality of a fit, the effects of noise, and the effects of incomplete data).
- How to compare two data sets using model fitting.
Offline practice:
- Exercises to pertaining to 1. and 2. above (provided)
- Advanced case studied (provided)
- Analyse your own data
Live workshop (optional, 2.5 hours):
- Answer questions pertaining to exercises or case studies
- Answer questions pertaining to your own data
The online demonstration in the scheduled live interactive session will involve a combination of lectures and live demonstration of using GraphPad prism to fit mathematical models to data and perform statistical analysis.
Participants are encouraged to ask questions throughout. A 1-2 week break will be provided for participants to work through exercises, case studies, and their own data before the optional live workshop takes place to answer questions.
Please note that this live workshop will be arranged based on participant interest and tutor availability following the scheduled online demonstration.
NB: A GraphPad Prism license for 1 year is provided free of charge to participants after the initial online demonstration. Please email courses@medsci.ox.ac.uk
Please make sure you read all of the sections below relating to how the course will work, the survey and the attendance certificate.
COURSE OBJECTIVES
At the end of the course, participants will be able to
- Examine examples of biological data where model fitting is useful
- Discover how to fit biological data to models
- Learn how to compare two data sets using model fitting
PARTICIPANT NUMBERS
Maximum 60
HOW IT WILL WORK
Booking confirmation and reminder emails will send you the link for the live interactive session (held in Zoom) and joining instructions. To join the session, you can click on the link 15 minutes before the session.
Arrangements for the optional live workshop will be made following the scheduled online session.
WHAT YOU WILL NEED
A good internet connection, uninterrupted time, camera and microphone enabled on PC.
ATTENDANCE CERTIFICATE ON SURVEY COMPLETION
It is now a requirement that you complete the three short questions in the survey you receive after attending the course. Once you have submitted the survey, you will be sent an email with a link to your attendance certificate. This is to ensure we receive the feedback we need to evaluate and improve our courses. Survey results are downloaded and stored anonymously.
PLEASE NOTE
Where no cost is indicated in the shopping trolley, no deposit is required. However, two or more non-attendances or late cancellations without good reason will be logged and may mean you cannot attend any further MSD training that term. Please refer to our Terms and Conditions for further information.