Workshop: Analysis of single cell RNA-seq data
https://www.physalia-courses.org/courses-workshops/course18/
Dates
5th-9th February 2018
Instructors:
Dr. Vladimir Kiselev (Wellcome Trust Sanger Institute, UK)
Dr. Tallulah Andrews (Wellcome Trust Sanger Institute, UK)
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methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.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.
WORKSHOP FORMAT
The workshop 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.
WHO SHOULD ATTEND
This workshop 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.
REQUIREMENTS
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.
CURRICULUM
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
Lab 1 – Processing raw scRNA-Seq data
File formats: FastQ, BAM, CRAM
Demultiplexing
Reads QC
Read Trimming
Lab 2 – Read alignment
Alignment using STAR
Alignment using Kallisto
Tuesday 6th – Classes from 09:30 to 17:30
Lecture 2 – Read quantification
- Read & UMI counting
- Gene length & coverage
- Gene expression units
Lab 3 - Introduction to R/Bioconductor
Installing packages: CRAN, Bioconductor, github
Data-types
Matrices, Data.frames, Bioconductor classes
Lab 4 – Introduction to scater, ggplot2 and pheatmap
scater object
Intro to ggplot2 and pheatmap
Visualisation of scRNA-Seq
Wednesday 7th – Classes from 09:30 to 17:30
Interactive Lecture 3 - Expression QC, normalisation and batch correction
- Different normalisation methods
- Batch correction methods
- Evaluation methods for batch correction
Lab 5 – Analysis of GEO data - Download data from GEO, create a scater object and perform the analysis above
Thursday 8th – Classes from 09:30 to 17:30
Lecture 4 - Identifying cell populations and Feature selection
- Dimensionality reduction
- Clustering
- Identifying marker genes
- Differential expression
- Validation/follow-up
Lab 6 – Feature selection & Clustering analysis
- Comparison of clustering methods
- Comparison of feature selection methods
Lecture 5 - Pseudotime cell trajectories
- Waddington Landscape
- Pseudotime inference
- Differential expression through pseudotime
Lab 7 - Pseudotime analysis
- Comparison of pseudotime methods
Friday 9th – Classes from 09:30 to 17:30
Lecture 6 - Combining scRNASeq datasets
- Projecting cells to existing reference
Lecture 7 - Review, Questions and Answers. Open discussion
Lab 8 - Analysis of GEO datasets
Lecture 8 - Presentation of results from GEO datasets
For more information, please visit our website: https://www.physalia-courses.org/courses-workshops/course18/
