Educators:
Altuna Alkalin, Verdan Franke, Bora Uyar (RBC/deNBI-epi Scientists from Berlin)

Date:
23-25 September 2020

Location:
Online

Contents:
The general aim of the course is to equip participants with practical and technical knowledge to analyze single cell RNA-seq data. With this aim in mind, we will go through unsupervised machine learning methods to analyze high-dimensional data sets, and move on to statistical methods developed to analyze bulk RNA-seq. Lastly, we will introduce analysis techniques used for single cell RNA-seq.

There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. The programming will be mainly done in R.

Day 1: Intro to machine learning & data visualization for genomics
Day 2: Bulk RNA-seq analysis
Day 3: Single cell RNA-seq analysis

Learning goals:
The course will be beneficial for first year computational biology PhD students, and experimental biologists and medical scientists who want to begin data analysis or are seeking a better understanding of computational genomics and analysis of popular sequencing methods.r

Prerequisites:
Some statistics and R programming experience will be good to keep up with the course. Practicals will be done in R.

Keywords:
Computational genomics, RNA-seq, Machine learing,

Tools:
R/Bioconductor