Analysis of single cell RNA-seq data
23 -27 March 2020
Freie Universität Berlin, Königin-Luise-Straße 1-3, 14195 Berlin
In recent years single-cell RNA-seq (scRNA-seq) has become widely used for transcriptome analysis in many areas of biology. In contrast to bulk RNA-seq, scRNA-seq provides quantitative measurements of the expression of every gene in a single cell. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid. In this course we will cover all steps of the scRNA-seq processing, starting from the raw reads coming off the sequencer. The course includes common analysis strategies, using state-of-the-art methods and we also discuss the central biological questions that can be addressed using scRNA-seq.
The course will be delivered over the course of five days. Each day will include a lecture and laboratory component. The lecture will introduce the topics of discussion and the laboratory sessions will be focused on practical hands-on analysis of scRNA-seq data. These sessions will involve a combination of both mirroring exercises with the instructor to demonstrate a skill as well as applying these skills on your own to complete individual exercises. After and during each exercise, interpretation of results will be discussed as a group. Computing will be done using a combination of tools installed on the attendees laptop computer and web resources accessed via web browser.
Targeted Audience & Assumed Background
This course is aimed at researchers and technical workers who are or will be analyzing scRNA-seq data. The material is suitable both for experimentalists who want to learn more about data-analysis as well as computational biologists who want to learn about scRNASeq methods. Examples demonstrated in this course can be applied to any experimental protocol or biological system.
The requirements for this course are:
Working knowledge of unix (managing files, running programs)
Programming experience in R (writing a function, basic I/O operations, variable types, using packages). Bioconductor experience is a plus. Familiarity with NGS data and its analyses (using alignment and quantification tools for bulk sequencing data)
Please find example datasets here: https://support.10xgenomics.com/single-cell-gene-expression/datasets
Dr. Dana Silverbush (Broad Institute of MIT and Harvard)
Dr. Kirk Gosik (Broad Institute of MIT and Harvard)
Dr. Orr Ashenberg (Broad Institute of MIT and Harvard)