Islet sci-ATAC-seq 2019

Repository contains code and scripts accompnaying the manuscript Single-nucleus ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures by Rai and Quang et al (2019) published in Molecular Metabolism.

The code for deep learning strategy within the manuscript is available separately at ParkerLab/PillowNet.

Bulk Islet ATAC-seq processing

Bulk islet samples were processed using the Snakemake pipeline available at raivivek/ATACseq-Snakemake.

Single-nucleus ATAC-seq processing

Scripts that drive the corresponding analysis are contained within the control directory. Further details are included within each directory.

Requirements

Using our conda environment

Assuming that you have a Linux 64-bit system, download and install Anaconda 3:

wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh
bash Anaconda3-5.0.1-Linux-x86_64.sh

Create the base environment:

conda env create --file env/meta.yaml
source activate sciatac_seq

Additional packages

• Ataqv
• R dependencies

packages <- c(
  "ggplot2", "tidyverse", "mclust", "optparse"
)

for(package in packages) {
    install.packages(package, dep = T)
}

Questions?

If you see any bug or have any questions, feel free to contact us via GitHub issues or email.