Date: 27 - 31 March 2025

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Note: This iteration of the course is currently not open for booking. However, please register your interest here to be notified when spaces become available. Your registration ensures you will be the first to know.

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.

If you do not have a University of Cambridge Raven account please book or register your interest here.

Additional information

  • ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
  • Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
  • Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
  • Further details regarding eligibility criteria are available here.
  • Guidance on visiting Cambridge and finding accommodation is available here.

Keywords: HDRUK

Venue: Craik-Marshall Building

City: Cambridge

Country: United Kingdom

Postcode: CB2 3AR

Organizer: University of Cambridge

Host institutions: University of Cambridge Bioinformatics Training

Target audience: Everyone is welcome to attend the courses, please review the policies.

Event types:

  • Workshops and courses

Scientific topics: Bioinformatics, Data mining, Data visualisation, Functional genomics, Transcriptomics


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