Tutorial on Single-Cell Data Analysis
A hands-on introduction to the statistical foundations and practical workflow of single-cell data analysis in R.
This tutorial is organized by young-SIS, the young group of the Italian Statistical Society, in collaboration with CIBB 2026.
- When Tuesday, September 1, 2026
- Where Sapienza University of Rome
- Fee No registration fee
- Seats Limited availability
Registration for the tutorial is separate from CIBB 2026 registration and is not required to attend the main conference.
Abstract
The course introduces the statistical foundations of single-cell data analysis, providing a general framework for understanding the main characteristics of single-cell datasets and the methodological challenges they pose. As such, it is also suitable for participants approaching this type of data analysis for the first time.
Who should attend
This course is designed for:
- PhD students and early-career researchers in statistics, biostatistics, bioinformatics, or computational biology
- Master's students with a background in statistics or quantitative biology
- Researchers and practitioners approaching single-cell data analysis for the first time
- Participants of CIBB 2026 wishing to deepen their understanding of single-cell methodology before the main conference
No prior experience with single-cell data is required. Familiarity with R and basic statistical concepts is recommended.
Learning outcomes
By the end of the course, participants will be able to:
- Understand the main statistical challenges of single-cell data, including sparsity, high dimensionality, and technical variability
- Apply standard normalization and dimensionality reduction techniques to real datasets
- Perform clustering and cell type identification using both supervised and unsupervised methods
- Run differential expression analysis while appropriately controlling the type I error
- Conduct a complete analytical workflow in R using the Bioconductor SingleCellExperiment framework
Part 1
Andrea Sottosanti
Department of Statistical Sciences, University of Padova
The session begins with a brief introduction to the biological context, outlining the key concepts underlying the data collection process, from experimental design to the construction of the final single-cell data matrix. Attention is given to the structural properties of these data, including cellular heterogeneity, high dimensionality, sparsity, and technical variability.
The session then focuses on the core components of the analytical workflow. First, the need for normalisation is discussed, highlighting the statistical issues that arise when analysing raw count data. A range of normalisation techniques is introduced, with emphasis on their role in mitigating technical effects such as differences in sequencing depth.
Subsequently, data visualisation methods and dimensionality reduction techniques are presented, including generalised principal component analysis (GLM-PCA) and non-linear approaches such as t-SNE and UMAP. This stage is particularly relevant for exploring the structure of the data, identifying major sources of variability, and providing low-dimensional representations that facilitate interpretation and guide subsequent analyses.
The session then addresses the identification of cell populations. Both supervised and unsupervised approaches are considered, with particular focus on the supervised method SingleR, alongside a range of clustering algorithms representative of unsupervised strategies.
Finally, Count Splitting is introduced as a method for differential expression analysis, i.e. identifying genes whose expression differs across cell types while appropriately controlling the type I error.
Part 2
Dario Righelli
Department of Biology, University of Padova
This session focuses on the practical analysis of single-cell RNA sequencing data in R using Bioconductor. Starting from the concepts introduced in Part 1, participants will work through the main steps of a typical analysis workflow, including data import, quality control, normalization, dimensionality reduction, clustering, and cell type annotation.
The session introduces the SingleCellExperiment data structure as the main container for storing and handling single-cell data in Bioconductor, bringing together expression data, feature annotations, cell metadata, reduced-dimensional representations, and downstream results in a single coherent framework. Using this structure, participants will see how key analytical steps can be carried out in a reproducible way. The goal is to provide a practical understanding of how the statistical concepts presented in Part 1 are translated into concrete analysis steps on real single-cell datasets.
The session will be held in "bring your own device" mode. Participants will use their own laptops, and a list of the required R packages and libraries will be provided in advance so they can install everything before the course.
Program
Tuesday, September 1, 2026
| Time | Activity |
|---|---|
| 14:00 - 15:30 | Part 1 |
| 15:30 - 16:00 | Break |
| 16:00 - 17:30 | Part 2 |
Join the waitlist
Because the course has a limited number of seats, we are currently collecting expressions of interest through a waitlist. Filling in the waitlist does not guarantee a seat, but it ensures that you will be among the first to be contacted as soon as the registration opens, with all the practical details and instructions on how to secure your place.
The operational and logistical details of the course, including registration instructions, venue information, and the list of software and packages to install in advance, will be shared in the coming weeks.
If you are interested in attending the tutorial, please leave your contact details below. We will reach out to you directly when registration opens.
About CIBB 2026
This tutorial is organized as part of CIBB 2026, the 21st International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, taking place at Sapienza University of Rome on September 2-4, 2026.
Participants of the tutorial are warmly invited to attend the main conference. CIBB 2026 offers three days of keynote lectures, special sessions, and contributed talks spanning bioinformatics, biostatistics, medical informatics, and AI-driven biomedical research. Special registration rates are available for students and retired academics.