Single-cell RNA-seq analysis (IN-PERSON)

This event has already happened
Date/time:

Thursday 16 April 2026 9:30am

Location:

Research Informatics Training Room, Craik-Marshall Building

About

Dates: Thu 16, Fri 17, Mon 20 April 2026

Time: 09:30 - 17:30

Location: Research Informatics Training Room, Craik-Marshall Building

♿ The training room is located on the first floor and there is currently no wheelchair or level access.

Participants can make use of the computers in the training room, unless otherwise advised. Instructions on specific system requirements and downloads will be provided when a booking is secured.

Please ensure you meet the target audience criteria and prerequisites before registering for a course.

 

Description

Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.

How to book Target audience
  • Everyone is welcome to attend the courses.
Prerequisites

Essential

  • Basic understanding of high-throughput sequencing technologies.
  • A working knowledge of the UNIX command line.
    • If you are not able to attend our prerequisite course, please work through our Unix command line materials ahead of the course (up to section 7).
  • A working knowledge of R.
    • If you are not able to attend our prerequisite course, please work through our R materials ahead of the course.

Desirable

  • Experience with analysis of bulk RNA-seq data is strongly recommended. 
  • A working knowledge of running analysis on High Performance Computing (HPC) clusters.
Registration fees

Fees must be paid at registration. 

Free for registered University of Cambridge students
£ 65/full day for all University of Cambridge staff, including postdocs. 
£ 65/full day for all academic participants from external Institutions and charitable organizations.
£ 130/full day for all Industry participants.

Contact us

For further information about the courses, please email the Research Informatics Training Team.