urrutiag / destin

Toolkit for single-cell analysis of chromatin accessibility
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Destin

This is the Jiang lab scATAC-seq processing and cell clustering pipeline.

Manuscript

Urrutia, Eugene, et al. "Destin: toolkit for single-cell analysis of chromatin accessibility." Bioinformatics, btz141, 2019. link

Questions & issues

If you have any questions or problems when using destin, please feel free to open a new issue here. You can also email the maintainers of the corresponding packages -- the contact information is below.

Installation

The bioinformatic pipeline requires cloning the git repostory from github, where yourPathToDestinRepo is your path to the local cloned repository

Running the vignettes also requires cloning the git repository

cd yourPathToDestinRepo
git clone https://github.com/urrutiag/destin.git

install dependencies

installed <- rownames(installed.packages())
pkgs = c("cluster", "data.table", "ggplot2",
         "gridExtra", "irlba",  "Matrix",
         "parallel", "Rtsne")
pkgs <- setdiff(pkgs, installed)
if (length(pkgs))
  install.packages(pkgs, dep=c("Depends", "Imports"))

biocPkgs = c("ChIPpeakAnno", "GenomicAlignments", "rtracklayer")
biocPkgs <- setdiff(biocPkgs, installed)
if (length(biocPkgs)) {
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install(biocPkgs)
}

#ClusterR is an optional package:
if ( ! "ClusterR" %in% rownames(installed.packages() ) )
  install.packages("ClusterR", dep=c("Depends", "Imports"))

Running the R package requires either installing from the above git repostory locally

install.packages("yourPathToDestinRepo/package", repos = NULL, type = "source")
library(destin)

or downloading from github directly (note that this will not allow for the bioinformatics pipeline or the vignettes):

install.packages("devtools")
devtools::install_github("urrutiag/destin/package")
library(destin)

Dependencies

Overview

Bioinformatics Pipeline

Input: fastq files of entire experiment or individual fastq files by cell, set of 2 for each of paired reads

Output: bam files by cell, peaks file






Destin

Input: bam files by cell, peaks file

Output: cluster membership, differential accessibility

GWAS association

Determine whether GWAS results are associated with increased chromatin accessibility in a particular cell type cluster. We utilize 2 methods originally developed for scRNA-seq expression: ECWE and MAGMA.

Example Workflows

Citations

de Leeuw, C. A., et al. (2015). Magma: generalized gene-set analysis of gwas data. PLoS comput. biol., 11 (4), e1004219.

Skene, N. G. et al. (2016). Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. neurosci-switz,10, 16.

Developers & Maintainers