Single-cell RNA-seq analysis with Python

This five‑day virtual course offers a focused introduction to analysing single‑cell RNA sequencing (scRNA‑seq) data using Python and command‑line tools. It is designed for you if you are a researcher in biology, computational science, and data analysis who wants to understand and apply core scRNA‑seq workflows. You should have basic familiarity with the Unix command line and a foundational understanding of Python.

Throughout the course, we will guide you through the main steps of droplet‑based scRNA‑seq analysis, from raw sequencing reads to clustering and biological interpretation. You will learn about experimental design, data processing, quality control, normalisation, feature selection, dimensionality reduction, batch correction, clustering, marker gene identification, and differential expression. Additional sessions introduce spatial transcriptomics, trajectory analysis, multi‑omics integration, and the Single Cell Expression Atlas.

Training combines lectures, live discussions, and hands‑on exercises using EMBL‑EBI’s virtual infrastructure, allowing you to practise workflows without installing specialised software.

Virtual course
You will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and asynchronous communication via Slack.

'Single-cell RNA-seq analysis with Python 2027' is currently in development. Sign up for alerts to hear as this course develops.

Who is this course for?

This course is aimed at wet-lab researchers who are generating, planning on generating, or working with single cell RNA sequencing data.

Participants are required to have basic experience with a Unix/Linux command line. Basic knowledge of Python is essential.  We recommend you go through these free tutorials before attending the course:

• Basic introduction to the Unix environment
• Introduction to programming in Python

What will I learn?

Learning outcomes

After this course you should be able to:

• Explain the steps in the scRNA-seq pipeline
• Repeat the course analysis of scRNA-seq data from extraction to cluster maps
• Recognise decision-making steps along the analysis pipeline and justify your decisions, from experimental design to final visualisation
• Employ appropriate data standards for repository submission and contribution to global cell atlases

Course content

During this course you will learn about:

• Single cell RNA-seq experimental design
• scRNA-seq analysis pipelines for droplet-based data
• EMBL-EBI Single Cell Expression Atlas Service
• Single cell data submission

Trainers

This course opens applications on 28 September 2026. Click to register your interest using the button on the right-hand side of this course website to hear as the course develops.