Introduction to Peak Analysis Workshop
| Audience | Computational Skills | Prerequisites | Duration |
|---|---|---|---|
| Biologists | Intermediate | None | Introduction to R |
Learning Objectives
- Describe peak data and different file formats generated from peak calling algorithms
- Assess various metrics used to assess the quality of peak calls
- Compare peak calls across samples within a dataset
- Create visualizations to evaluate peak annotations
- Evaluate differentially enriched regions between two sample groups
These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning.
Lessons
Description
This repository has teaching materials for a hands-on Introduction to Peak Analysis workshop. This workshop will use the R statistical programming environment to evaluate files generated from peak calling of ChIP-seq (and related approaches i.e. CUT&RUN and ATAC-seq) data. We will provide participants with a suite of tools and a basic workflow beginning with quality metrics through to annotation and visualization. This workshop will introduce participants to:
- File formats for peak data
- Approaches to check peak quality and reproducibility across replicates
- Peak annotation methods and tools for visualization
- Differential peak enrichment analysis and functional analysis
Working knowledge of R is required or completion of the Introduction to R workshop.
Note for Trainers: Please note that the schedule linked below assumes that learners will spend between 3-4 hours on reading through, and completing exercises from selected lessons between classes. The online component of the workshop focuses on more exercises and discussion/Q & A.
Dataset
The R project for this workshop can be downloaded with this link.
Installation Requirements
Download the most recent versions of R and RStudio for your laptop:
NOTE: When installing the following packages, if you are asked to select (a/s/n) or (y/n), please select “a” or “y” as applicable.
(1) Install the below packages on your laptop from CRAN. You DO NOT have to go to the CRAN webpage; you can use the following function to install them:
install.packages("BiocManager")
install.packages("tidyverse")
install.packages("pheatmap")
install.packages("UpSetR")
install.packages("RColorBrewer")
install.packages("ggrepel")
Note that these package names are case sensitive!
(2) Install the below packages from Bioconductor. Load BiocManager, then run BiocManager’s install() function 7 times for the 7 packages:
library(BiocManager)
install("insert_first_package_name_in_quotations")
install("insert_second_package_name_in_quotations")
& so on ...
Note that these package names are case sensitive!
BiocManager::install("ChIPseeker")
BiocManager::install("ChIPpeakAnno")
BiocManager::install("DiffBind")
BiocManager::install("clusterProfiler")
BiocManager::install("TxDb.Mmusculus.UCSC.mm10.knownGene")
BiocManager::install("IRanges")
BiocManager::install("GenomicRanges")
BiocManager::install("DESeq2")
NOTE: The library used for the annotations associated with genes (here we are using
TxDb.Mmusculus.UCSC.mm10.knownGene) will change based on organism. The list of different organism packages are given here.
(3) Finally, please check that all the packages were installed successfully by loading them one at a time using the library() function.
library(tidyverse)
library(pheatmap)
library(UpSetR)
library(ChIPseeker)
library(ChIPpeakAnno)
library(DiffBind)
library(clusterProfiler)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(IRanges)
library(GenomicRanges)
library(DESeq2)
library(RColorBrewer)
library(ggrepel)
(4) Once all packages have been loaded, run sessionInfo().
sessionInfo()