scATAC

A collection of Notebooks to analyse scATAC-seq data.

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. References
5. Contact

About The Project

This is a collection of Notebooks made to analyse scATAC-seq data. They are based on the vignettes from different R-algorithms: Signac, Seurat, Cicero and Monocle3.

0. Pre-analysis: quality checks, filters, dimensional reduction and gene activity quantification.
1. Data Integration: add scRNA-seq information and compute co-embedding.
2. Coaccessibility: scores co-accessibility between peaks to predict cis-regulatory interactions, such as those between promoters and enhancers. 2.1 Trajectories: tries to infer trajectories using chromatin accessibility to compute pseudotime.
3. Differentially Accessible Peaks: differences between chromatin accessibility in groups or clusters of cells.
4. Motif Analysis: overrepresented motifs in a cluster of cells in comparison to another cluster of cells. Computes motif activities using ChromVAR.
5. Transcription Factor Footprints: finds TF footprints. Similarly to nucleosomes, bound TFs hinder cleavage of DNA, resulting in defined regions of decreased signal strength within larger regions of high signal-known as footprints, Bentsen et. al, 2020.

Built With

• Anaconda 4.10.1
• R 4.0.2

Getting Started

Install the environment and follow the Notebooks.

Prerequisites

Anaconda. If you haven't installed Anaconda yet, you can follow the next tutorial: Anaconda Installation.

Installation

1. Clone the repo

   git clone https://github.com/loremendez/Gemstones.git

2. Install the environment
   
   1. Create and activate the environment

      conda create -n atac_env r-base=4.0.2
      conda activate atac_env

   2. Install Signac, genome assembly and gene annotation packages following the instructions on the website.
   3. Install additional packages in R console. Important: do not update any packages.

      if (!requireNamespace("remotes", quietly = TRUE)) {install.packages("remotes")}
      remotes::install_github('satijalab/seurat-wrappers')
      remotes::install_github("mojaveazure/seurat-disk")
      BiocManager::install(c("motifmatchr", "TFBSTools", "JASPAR2020", "chromVAR"))

   4. Install Monocle3 following the instructions or:

      conda install -c bioconda r-monocle3

   5. Install Cicero in R console.

      remotes::install_github("cole-trapnell-lab/cicero-release", ref = "monocle3")

   6. Install jupyter-lab.

      conda install jupyterlab

   7. Create a Kernel from R console (optional). sh install.packages('IRkernel') IRkernel::installspec(name='atac_seq', displayname='atac_seq')

Usage

Activate the environment, open Jupyter-lab and the notebooks in order.

jupyter-lab

References

[1] Signac. Seurat Cicero Monocle3

Contact

Lorena Mendez - LinkedIn - lorena.mendez@tum.de

Take a look into my other projects!