1. Program
Session 1: Processing bulk and single-cell ATAC-seq reads
Lecture 1: Bulk ATAC-seq experimental approaches
15’
- Introduction to bulk ATAC-seq methods: “classic”, omniATAC-seq, low input ATAC-seq, …
Hands-on 1: Processing bulk ATAC-seq data
35’
- Sequencing QC
- Mapping reads and correcting
- Visualizing and QC-ing the results
Exercise 1: Inspecting bulk ATAC-seq outputs
10’
- Visually inspect the generated tracks and the identifed peaks
- Get the distribution of fragment sizes over ATAC peaks.
- Compare this distribution to the previous one. Discuss.
Lecture 2: Single-cell ATAC-seq experimental approaches
20’
- Introduction to “single cell” ATAC-seq methods: droplet-based scATACseq, indexed scATACseq, joint scRNAseq and scATACseq
Hands-on 2: Processing single-cell ATAC-seq data
30’
- 10X scATACseq: from reads to matrix counts with
cellranger
- QC-ing with
Loupe
Exercise 2: bulk versus single-cell ATAC-seq
10’
- Compare aggregated signal obtained from single-cell ATAC-seq to that of bulk ATAC-seq
Homework: Download raw data from GSE and process it
- Install
cellranger and mapping tools
- Download reads from GSE129785 and GSE87646
Session 2: Peak-centered differential accessibility analysis
Lecture 3: Clustering single-cell data
20’
- Normalizing scATACseq data
- Clustering approaches
- Annotating cell types
Hands-on 3: Downstream analysis of scATACseq data
30’
- Reading
cellranger output in R
- Normalizing scATACseq counts
- Clustering cells by their accessible loci
- Differential accessibility assay with
Seurat
Exercise 3: Recovering genes associated with cluster-specific peaks
10’
- Identify the genes associated with cluster-specific peaks
Lecture 4: ATAC-seq and accessibility peaks
20’
- Peaks in bulk ATAC-seq vs single-cell ATAC-seq
- Integrating peak sets from different experiments
- Finding enriched sequences
Hands-on 4: Peaks differential accessibility analysis
30’
- Quantifying accessibility at ATAC peaks
- Differential accessibility assay
- Clustering peak sets
Exercise 4: Peak location vs genomic features
10’
- Find which types of chromatin stats are enriched in accessible loci
Homework
- Group peaks by accessibility scores in different samples / cells
- Perform gene ontology over-representation analyses (GOA)
- Perform gene Set Enrichment Analyses (GSEA)
Session 3: Advanced topics and multi-omics integration
Lecture 5: Motif enrichment analysis
20’
- The good, the bad and the ugly: Ref. sequence, ref. annotations and ref. version!
- Tools to perform motif enrichment analysis
- Public databases for transcription factor binding sites (TFBS)
Hands-on 5: Find motifs enriched in peak sets
30’
- Use web-based or command-line
meme software to find de novo motifs enriched in a set of peaks
- Scan sets of peaks for known TF binding sites
Hands-on 5: Find motifs enriched in peak sets
10’
- Compare de novo found motifs to public databases with
fimo
Topic 6: Inferring regulatory networks from scATACseq
1h’
Homework
- Find TF motifs enriched in peaks with increased accessibility in a specific cluster
- Run chromVAR on a single-cell dataset
- Leverage the motifmatchr package to replicate the ugly nested apply functions in Hands-on # 5