Course Objectives & pre-requisites
NB – The listing below is a draft. It should be broadly correct but may change before the Summer School is run.
It is advisable to have basic familiarity with R and the command line.
An R Crash course is available here and a good introduction to the Linux command line is here.
- Key aspects of experimental design
– Experimental variables
– Power: variance and replicates
– Bias: confounding factors, randomisation, and controls
- Design parameters for Functional sequencing experiments
- Experimental design process at CRUK-CI
Data processing for Next Generation Sequencing
- A brief introduction to file formats
- Quality control and artefact removal
- Short read alignment to a reference genome
RNAseq using R
- Learn how to analyse RNA-seq count data, using R.
- Reading data into R, quality control and performing differential expression analysis and gene set testing, with a focus on the edgeR analysis workflow.
- You will learn how to generate common plots for analysis and visualisation of gene expression data, such as boxplots and heatmaps.
- You will also be learning how alignment and counting of raw RNA-seq data can be performed in R.
- This workshop is aimed at biologists interested in learning how to perform differential expression analysis of RNA-seq data when reference genomes are available
Single Cell RNAseq
- Normalise scRNA-seq data using the scater package
- Visualise the data and apply dimensionality reduction
- Use available tools for analysing differential expression
- Use available methods for clustering
- Use available methods for pseudo-time alignment
ChIP-seq and ATAC-seq Analysis
- Introduction to ChIP-seq
- Peak Calling
- Quality control methods for ChIP-seq
- Useful software fora nalysis of genomic data
- Downstream analysis of ChIP-seq and ATAC-seq data
- Identifying direct targets of Transcription Factors
- Differential binding analysis
- Introduction to Epigenomics and Chromatin Interactions