Analysis of single cell RNA-seq data

Dates

5-9 February 2018

 

Course overview

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methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.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.

Course Format

The course will be delivered over the course of five days. Each day will include an introductory lecture with class discussion of key concepts. The remainder of each day will consist of practical hands-on sessions. 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 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 course is intended for those who have basic familiarity with Unix and bash and R scripting languages. We will also assume that you are familiar with mapping and analysing bulk RNA-seq data as well as with the commonly available computational tools.

EXAMPLE DATA

Attendees will learn to process, analyze, visualize and interpret results from one of the Gene Expression Omnibus (GEO) publicly available single cell datasets. These datasets were generated from different organisms and tissues. These data are representative of multiple scRNASeq protocols and various experimental designs. They will be analyzed to determine previously known as well as potentially novel interpretations.

WHERE

Potsdamer Straße 98a, 10785, Berlin

program

 

Monday 5th – Classes from 09:30 to 17:30


Lecture 1 – scRNA-Seq experimental design and raw data processing

 

  • General introduction
  • Comparison of Bulk and single cell RNA-Seq
  • Overview of available technologies and experimental protocols
  • scRNA-Seq experimental design
  • scRNA-Seq general computational workflow
  • Common single-cell analyses and interpretation

 


Instructors

 

Dr. Davis McCarthy

Cost overview

Analysis of single cell RNA-seq data

Course material and refreshments

 

Analysis of single cell RNA-seq data

All-inclusive

          480 € (VAT included)

      795 € (VAT included)


Please click HERE to get all the information about our packages.

Application deadline is: January 12th, 2018.

 

Cancellation Policy:

 

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

 

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.