BIO-603(LG) / 1 credit
Teacher: La Manno Gioele
Remark: 3-day Block course, every year in January. To register, contact EDMS Administration
Give students a feel for how single-cell genomics datasets are analyzed from raw data to data interpretation. Different steps of the analysis will be demonstrated and the most common statistical and bioinformatic techniques applied by the students. Data analysis in single-cell genomics.
The students will be provided with a modest-sized single-cell transcriptomics raw dataset for without any biological context given initially. The students will learn how to use analysis tools and statistical methods to understand the biological process the dataset is capturing.
Starting from read-mapping, they will perform steps of data processing and quality control so to end up with a gene expression table.
On this data, the students will learn how to perform feature selection and data visualization. The combination of multivariate analysis routines and literature based search will lead to the an interpretation of the observed gene expression variation.
Open to max. 4 students. Please note that you are not allowed to inscribe in your own group!
single-cell, genomics, data analysis, transcriptomics
A course that included python or R programming project.
By the end of the course, the student must be able to:
- Perform a basic analysis of a single cell RNA-seq dataset autonomously
Butler et al, Nature Biotechnology, 2018
In the programs
- Exam form: Project report (session free)
- Subject examined: Practical - LaManno Lab
- Lecture: 6 Hour(s)
- Practical work: 18 Hour(s)