Computational methods for single-cell biology, Lectures and project work

Fabian TheisMaria Colome TatcheCarsten Marr
E-mail: E-mail: maria.colome@helmholtz-muenchen.deE-mail: 


Benjamin SchubertAntonio Scialdone
E-mail: E-mail: 


Room:                Virtual
Date and Time:  Wednesdays,  14:00 - 15:30; 04.11.2020 to 10.02.2021
Prerequisites:    Bachelor in mathematics, bioinformatics, computer science, physics, statistics or a related field. One lecture on machine learning (by Theis or Guennemann). Strong interest in biological and biomedical research questions. 
Target Group:   Mathematics, biomathematics, bioinformatics, physics, computer science/ data engineering.
ECTS:                 6
Number of participants: 20
Language:         English




We design this as a two part module:

(1) Six lectures introduce the students to the most relevant topics and methods for single cell data analysis.  This is followed (2)  an eight week project work where students work on specific single-cell topics at one ICB lab for 8 weeks to get hands-on experience. The content of the lectures is:

  • Single cell biology
  • scRNA-seq analysis 1
  • scRNA-seq analysis 2
  • Single cell Immune profiling
  • Single cell epigenomics
  • Single cell image computing       

Learning Outcomes:

At the end of the module students are able to:

  • Describe single cell RNA sequencing, as well as single cell epigenome sequencing and single cell immuno-profiling, and their applications
  • Describe single cell imaging techniques, data types, and applications.
  • Apply standard preprocessing methods (normalization and correlation of batch effects) as well as quality control of the single cell sequencing data and assess their performance.
  • Describe and apply methods for dimensionality reduction, clustering, ordering (pseudotemporal ordering), visualization and differential analysis (differential expression, differential chromatin openness and differential DNA methylation) in single cell sequencing data and assess their performances
  • Describe the concept of spatial transcriptomics.
  • Describe and apply deep learning methods for feature extraction and classification of images and  assess their performance.
  • Describe the different biomedical applications of single-cell data and identify the best algorithms for a specific problem.
  • Carry on a small single cell research project using real biomedical data.