This course covers first the classic genetics, followed by more advanced contemporary genomics techniques. This course also introduce the R programming language, followed by basic bioinformatics for the analysis of genomics data.
The students are exposed to experimental and analytical approaches specific to single cell biology, with an emphasis on quantitative aspects.
The course is organized in four parts, each containing alternating lectures and journal clubs presented by the students in a 1:1 ratio (see Teaching methods below for more details). Part 1 (weeks 1-4) will focus on the fundamental and biomedical research values of single cell genomic and transcriptomic analyses. Part 2 (weeks 5-8) will focus on dynamic analysis of gene expression, signaling and cell fate choices in single cells. Part 3 (weeks 9-10) will focus on engineering approaches to single cell analysis. Finally, part 4 (weeks 11-12) will focus on non-genetic heterogeneity in bacteria and its consequences. Week 13 will consist of a half-day symposium featuring external and internal speakers (week 13) and an oral exam (week 14).
By the end of the course, the student must be able to:
- Explain the limitations of bulk analysis that can be overcome by single cell analysis
- Explain the advantages and limitations of single cell analysis in gathering quantitative data
- Explain how single cell analyses can have diagnostic or biomedical value
- Propose experimental approaches to investigate phenotypic heterogeneity in a cell population
- Propose experimental approaches to investigate temporal fluctuations in gene expression
- Propose experimental approaches to investigate cell fate choices and bacterial resistance to drugs at the single cell level
ASAP hands-on sessions
ASAP (asap.epfl.ch) is a web portal for the interactive analysis of single-cell RNA-seq data
- Provides the Human Cell Atlas (HCA) with a common interface for reproducible and interactive analysis of data
- Centralized computational resources: Ruby-on-rails server hosted at EPFL + any C/R/Python/Java-implemented tool
- Job queuing management: delayed-jobs gem
- Currently ~1900 projects from registered users (~400 registered users)
- Supported by the Chan-Zuckerberg Initiative (CZI)
Please download the dataset for the course: Raw count matrix
Description: Dataset of 91 cells containing 3 different cell types, one of which can be divided in 2 or 3 subpopulations.
Thus, a good PCA/tSNE plot should show 3-4 or better 5 clusters of cells.
Your task is to uncover correctly the different cell types present in the dataset.