Single-cell RNA-seq Data Analysis (SCRNPR)
Practical single-cell RNA-seq data analysis with Seurat, quality control, normalization, batch correction, dimensionality reduction, clustering, and multi-dataset integration.
Duration: 25 hours
Format: Recorded ‘on-demand’ Format
£350Registration Fee
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Course Description
Single-cell RNA sequencing (scRNA-seq) makes it possible to investigate cellular diversity, identify cell populations, characterize cell states, and explore biological variation at single-cell resolution. This practical course introduces the analytical principles and computational workflows used to process, analyse, and interpret scRNA-seq datasets, with a strong emphasis on the Seurat ecosystem in R.
Participants will work through key stages of a single-cell analysis workflow, from quality control and normalization to dimensionality reduction, clustering, and integration across datasets. The course compares commonly used methods, discusses their assumptions and limitations, and explores how scRNA-seq data can support cell-type deconvolution and composition estimation in bulk RNA-seq. Practical exercises emphasize reproducible analysis, critical method selection, and biological interpretation.
What You’ll Learn
- The principles of single-cell RNA-seq technologies and the structure of a Seurat analysis workflow
- How to assess data quality and filter low-quality cells and genes in scRNA-seq data
- How to detect cell-cycle effects and identify potential doublets in scRNA-seq data
- \How deconvolution, scNorm, and SCTransform normalization approaches differ for scRNA-seq data• How to correct batch effects and assess their impact on biological signals in scRNA-seq data
- How to estimate cell-type composition in bulk RNA-seq using single-cell reference data• How PCA, t-SNE, and UMAP reveal different aspects of cellular variation using scRNA-seq data
- Why UMAP is widely used for scRNA-seq visualization but requires caution in population-genomics analyses
- How hierarchical, partitioning, spectral, consensus, density-based, and graph-based clustering compare for scRNA-seq data
- How to integrate scRNA-seq datasets horizontally and vertically using ComBat, Harmony, Scanorama, UMAP-based approaches, autoencoders, and MOFA
.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is designed for biologists, molecular biologists, ecologists, geneticists, biomedical researchers, bioinformaticians, computational biologists, postgraduate students, and early-career researchers who want to develop practical skills in single-cell RNA-seq data analysis. It is particularly relevant to those working in genomics, transcriptomics, developmental biology, immunology, cancer biology, and related life sciences.
Participants should have a basic understanding of molecular biology and gene expression, and some familiarity with R programming is recommended. Previous experience with single-cell analysis is helpful but not required, as the core concepts and workflows will be introduced during the course.
Equipment and Software requirements
A laptop or desktop computer with internet access is required. The course uses R and RStudio, with practical workflows that may also be run in cloud-based environments such as Google Colab where appropriate. Installation and setup instructions will be shared ahead of the course.
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 simultaneously.
The required R packages, including Seurat and packages used for normalization, integration, visualization, and clustering, will be introduced during the workshop. A package list and optional pre-installation instructions will be shared with participants in advance.
Dr. Nikolay Oskolkov
Nikolay is a bioinformatician, computational biologist, and data scientist working at the intersection of biology, medicine, statistics, and artificial intelligence. His research focuses on applying mathematical statistics, machine learning, and deep learning methods to complex biological and biomedical datasets, including genomics, transcriptomics, microbiome research, single-cell data, metagenomics, and multi-omics integration.
Nikolay has a PhD in theoretical physics from 2007, he transition to the Life Sciences in 2011. He currently leads the Metabolic Research Group (MRG) within the TARGETWISE project at the National Institute of Research and Innovation in Latvia, and having a teaching position at Lund University, Sweden, he has previously held research positions at the Danish Technical University, University of North Carolina, Lund University and the National Bioinformatics Infrastructure Sweden (NBIS/SciLifeLab).
Nikolay has more than 20 years of teaching experience and is widely recognised for his ability to communicate advanced statistical and computational methods to researchers from diverse scientific backgrounds. His expertise spans both frequentist and Bayesian statistics, machine learning, dimensionality reduction, clustering, bioinformatics, and scientific programming in R and Python. He has delivered numerous international workshops, summer schools, and professional training courses in computational biology, genomics, and AI-driven biomedical research.
Education & Career
- PhD in Theoretical Physics (2007)
• Transitioned from theoretical physics to bioinformatics and computational biology in 2011
• Group Leader (PI), Metabolic Research Group, TARGETWISE Project, Latvia
• Former researcher and bioinformatician at Lund University and NBIS/SciLifeLab, Sweden
• Author of more than 60 peer-reviewed scientific publications with extensive international collaborations in computational biology and biomedical research
Research Focus
Nikolay’s work centres on extracting biological insight from large-scale, high-dimensional datasets using advanced statistical and machine learning approaches. His research interests include:
- Machine learning and deep learning for biomedical and omics data
• Multi-omics integration and systems biology
• Single-cell transcriptomics and dimensionality reduction methods
• Population genomics and evolutionary biology
• Microbiome, environmental DNA, and ancient DNA analysis
• Statistical modelling and Bayesian approaches for complex biological systems
• AI applications in precision medicine and drug discovery
Current Projects
- Development of machine learning methods for multi-omics data integration and drug discovery in metabolic diseases
• AI-driven approaches for genomics and computational biology
• Statistical and computational methods for ancient and environmental DNA research
• Machine learning analysis workflows for single-cell and population genomics datasets
• Research on metabolic diseases through integrative bioinformatics and systems biology approaches
Professional Consultancy
Nikolay provides expert consultancy in biological and biomedical data analysis, supporting academic researchers, healthcare scientists, and industry teams. His consultancy expertise includes:
- Bioinformatics and computational biology
• Medical genomics and precision medicine
• Single-cell and multi-omics data analysis
• Metagenomics and population genomics
• Frequentist and Bayesian statistical modelling
• Machine learning and deep learning applications
• Scientific programming in R, Python, Bash, and C++
• Study design, data analysis pipelines, and reproducible research workflows
Teaching & Skills
- More than 20 years of teaching experience in statistics, machine learning, and computational biology
• Teaches topics including machine learning, deep learning, Bayesian statistics, dimensionality reduction, clustering, single-cell analysis, genomics, and bioinformatics
• Instructor for international courses and workshops through organisations including Instats, Physalia, NBIS SciLifeLab, TARGETWISE, and RaukR
• Strong advocate for rigorous statistical thinking, reproducible research, and accessible scientific education
• Experienced in translating advanced computational methods into practical tools for life scientists and healthcare researchers
Links
Session 1 – 02:30:00 – Introduction to single-cell RNA-seq technologies
Overview of single-cell RNA-seq technologies, experimental design, count matrices, common sources of technical variation, and the main stages of a typical analytical workflow.
Session 2 – 02:30:00 – Seurat workflow and quality control
Introduction to Seurat and practical quality control: filtering low-quality cells and genes, evaluating quality metrics, detecting cell-cycle effects, and identifying potential doublets.
Session 3 – 02:30:00 – Normalization and batch effects
Why normalization is required for scRNA-seq and how it is different from bulk RNAseq data; comparison of deconvolution-based normalization, scNorm, and SCTransform; assumptions, strengths, limitations, and practical consequences for downstream analysis.
Session 4 – 02:30:00 – Cell-type deconvolution of bulk RNA-seq
Using scRNA-seq reference data to estimate cell-type composition in bulk RNA-seq; deconvolution principles, workflow considerations, interpretation, and limitations.
Session 5 – 02:30:00 – Linear and non-linear dimensionality reduction
PCA, t-SNE, and UMAP for exploring cellular variation and discovering cell populations; comparison of their objectives, assumptions, strengths, and limitations.
Session 6 – 02:30:00 – Interpreting low-dimensional embeddings
Practical comparison of PCA, t-SNE, and UMAP for cell-population discovery and visualization. Discussion of why UMAP is widely used in scRNA-seq, and why its properties require caution when interpreting population-genomics data.
Session 7 – 02:30:00 – Clustering methods for scRNA-seq
Comparison of basic clustering approaches, including hierarchical and partitioning methods, with advanced spectral, consensus, density-based, and graph-based clustering approaches.
Session 8 – 02:30:00 – Practical clustering and cluster interpretation
Applying and comparing clustering approaches to scRNA-seq data; choosing resolution and distance measures, assessing cluster stability, and interpreting clusters as candidate cell populations or cell states.
Session 9 – 02:30:00 – Horizontal integration of scRNA-seq datasets
Why datasets need to be integrated; comparison of ComBat, Harmony, and Scanorama for addressing batch effects and aligning datasets while preserving biologically meaningful variation.
Session 10 – 02:30:00 – Vertical integration and latent-factor methods
Vertical integration of complementary data representations and modalities, including UMAP-based approaches; comparison of deep-learning autoencoders with MOFA for learning shared structure across scRNA-seq datasets. Discussion of assumptions, interpretation, and method selection.
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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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