ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) This course will be delivered live
2 December 2024 - 4 December 2024
£380.00
Event Date
Monday, December 2nd, 2024
COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
About This Course
Take your RNA-Seq analysis to the next level with single cell RNA-Seq. This technology allows insights with an unpredicted level of detail, but that brings a new level of complexity to the data analysis. In this course, we will learn about the most popular single cell platforms, how to plan a scRNA-Seq experiment, deal with some of the many pitfalls when analysing your data, and effectively gain exciting, and cell type specific biological insights
By the end of the course participants should:
- Understand the basic principles of popular single cell platforms and the pros and cons of the different technologies.
- Be able run standard software to process raw 10x Genomics and Parse Bioscience data and interpret the outputs
- Understand how to use the ‘Trailmaker’ to quickly analyse scRNA-Seq data.
- Understand the basics of the R Bioconductor ‘Seurat’ package, and how to combine it with other tools.
- Understand how to perform appropriate data quality control and filtering.
- Understand how to cluster cells both within and between samples, and identify possible cell types of individual cells and clusters
- Understand how to use statistically robust methods to compare gene expression between sample to identify cell type specific changes in gene expression and potential pathways of interest.
Intended Audiences
Academics, post-graduate students or biotech employees working on, or planning to work on any type of single cell RNA-Seq data.
Venue
Delivered Remotely
Course Details
Availability – 20
Duration – 2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English
Teaching Format
Mixture of lectures covering the theory, and practical sessions using the Linux command line and RStudio. Practical sessions are a mixture of demonstrations by the tutor and exercises to be completed independently. Data sets for computer practical sessions will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Participants should have a basic understanding of transcriptomics and molecular biology
Assumed computer background
Equipment and software requirements
Participants should have basic experience of R, RStudio and linux.
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
COURSE PROGRAMME
Day 1 Classes from 9:30 – 3:30
- Basic principles of popular single cell platforms and the pros and cons of the different technologies.
- Important considerations when planning a scRNA-Seq experiment.
- Running standard software to process raw 10x Genomics and Parse Bioscience data and interpretate outputs to perform an initial assessment of data quality.
- Sample and library preparation issues that may affect your data, and how these issues may be detected
- Use of the ‘Trailmaker’ tool to perform further analysis without the need for programming skills.
Day 2 Classes from 9:30 – 3:30
- Understand the basics of the R Bioconductor ‘Seurat’ package, and how to combine it with other tools.
- Normalise data and cluster cells.
- Predict cell types of individual cells.
- Clean and filter your data in a manner appropriate for your particular sample and tissue type.
Day 3 Classes from 9:30 – 3:30
- Integration of and co-clustering of cells from multiple samples.
- Identification of cell type marker genes and annotation of clusters.
- Use of statistically robust methods to compare gene expression between samples.
- Identification cell type specific changes in gene expression and potential pathways of interest.
- Discussion of participants individual projects (optional).
Course Instructor
EDINBURGH GENOMICS