News:Workshop: Analysis of single cell RNA-seq data
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Workshop: Analysis of single cell RNA-seq data


5th-9th February 2018


Dr. Vladimir Kiselev (Wellcome Trust Sanger Institute, UK)
Dr. Tallulah Andrews (Wellcome Trust Sanger Institute, UK)


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methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.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


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.


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.


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.


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:

R next-gen RNA-Seq alignment • 4.3k views

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