Single cell RNA-Seq analysis (SCRN02)
Learn single cell RNA-Seq analysis with Seurat, 10x Genomics, and advanced QC methods. Gain cell type-specific insights in this live online course.
Duration: 4 Days, 3.5 hours per day
Next Date: March 16-19, 2026
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£350Registration Fee
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- Overview
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Course Description
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.
What You’ll Learn
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 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.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is intended for academics, postgraduate students, and biotech professionals who are working with, or planning to work with, single-cell RNA-Seq data. Participants are expected to have basic experience with R, RStudio, and Linux, along with a foundational understanding of transcriptomics and molecular biology.
Equipment and Software requirements
A laptop or desktop computer with a reasonable internet connection and a web browser. No other software is required.
While not essential, using a large monitor – or ideally a dual-monitor setup – can significantly enhance your learning experience by allowing you to view course materials and work in R or linux simultaneously.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
All necessary software is installed already on a virtual machine that you will access through your web browser
Dr. Frances Turner
Frances is a bioinformatician at Edinburgh University with extensive experience supporting researchers across the life sciences in analysing and interpreting high-throughput sequencing data. Her work focuses on developing and applying bioinformatics pipelines and analytical approaches that enable scientists to extract meaningful biological insights from complex genomic datasets. She works with a wide range of sequencing applications, including whole-genome, transcriptome, and metagenomic data, and has particular expertise in ensuring data quality, reproducibility, and effective downstream analysis.
Frances has spent many years collaborating closely with research groups from diverse disciplines, providing both technical expertise and strategic advice on study design, sequencing strategy, and data interpretation. Alongside her consultancy and applied work, she is also committed to training and knowledge exchange, delivering workshops and guidance to help life scientists build confidence in handling and analysing their own sequencing data.
Education & Career
- PhD in Bioinformatics
- MSc Bioinformatics
- Bioinformatician at Edinburgh University, providing analysis support and training for high-throughput sequencing projects at the Gene Therapy Mission Hub.
Research Focus
Frances’ work centres on enabling researchers to make the most of next-generation sequencing technologies. She is particularly interested in:
- Developing and refining bioinformatics workflows for diverse sequencing applications
- Supporting reproducible and scalable approaches to data analysis
- Bridging the gap between experimental design and downstream interpretation
Current Projects
- Providing end-to-end analysis support for whole-genome, transcriptomic, and metagenomic studies
- Collaborating with life science researchers on custom data workflows
- Contributing to training programmes that build bioinformatics capacity in the research community
Professional Consultancy
Frances provides expert bioinformatics support to academic and applied research projects, offering guidance on sequencing design, data processing, and statistical interpretation. She regularly collaborates with interdisciplinary teams, ensuring robust, reproducible, and biologically meaningful outcomes.
Teaching & Skills
- Teaches workshops on sequencing data analysis, quality control, and reproducible pipelines
- Skilled in a wide range of bioinformatics tools and platforms for genomic and transcriptomic analysis
- Advocates for open science, reproducibility, and empowering life scientists to engage confidently with their data
Links
• LinkedIn
Session 1- 01:20:00 – Introduction to scRNA-seq
Overview of basic principles of popular single cell platforms, the pros and cons of the different technologies, and important considerations when planning a scRNA-Seq experiment.
Session 2 – 01:10:00 – Basic data processing
Understanding of the structure of raw scRNA-Seq data, the running standard software to process raw 10x Genomics and Parse Bioscience data and interpretation outputs to perform an initial assessment of data quality.
Session 3 – 01::00:00 – Introduction to Seurat
Understanding the basics of the R Bioconductor ‘Seurat’ package, and visualisation of key data QC metrics
Session 4 – 01:15:00 – Cell type annotation
Understanding how to use SingleR and how to interpret QC metrics with reference to cell type.
Session 5 – 01:15:00 – Cell clustering
Understanding how to cluster data and interpret QC metrics at the level of cell clusters
Session 6 – 00:30:00 – Data filtering
Understanding how to filter data to remove like doublets or damaged cells.
Session 7 – 00:30:00 S- ample integration
Understanding integration of data from multiple samples and correction for batch effect of and co-clustering of cells from multiple samples.
Session 8 – 01:30:00 – Marker gene identification and visualisation
Understanding identification of cluster marker genes and visualisation of expression of known marker genes at the level of clusters,
Session 9 – 01:00:00 – Automatic cluster identification
Automatic cluster identification using ScType
Session 10 – 01:00:00 – Differential expression analysis and pseudo bulk analyis
Use of statistically robust pseudo bulk analysis to compare gene expression between samples using Seurat
Session 11- 01:00:00 – Multivariate differential expression analysis analysis
Exporting of data from Seurat for analysis of more complex experiments in DESeq2
Session 12 – 01:30:00 – Functional analysis
Identification cell type specific changes at the level of gene function and pathways with GSEA
Session 13 – 00:30:00 – Discussion of participants projects.
A chance to discuss your own projects and ask more detailed questions relating to your own work
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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