https://github.com/ManchesterBioinference/Scasat

Scasat is a single cell ATAC-seq preprocessing and analysis pipeline
https://github.com/ManchesterBioinference/Scasat

atac-seq pipelines single-cell

Last synced: about 1 month ago
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Scasat is a single cell ATAC-seq preprocessing and analysis pipeline

• Host: GitHub
• URL: https://github.com/ManchesterBioinference/Scasat
• Owner: ManchesterBioinference
• Created: 2017-11-29T23:33:53.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-02-27T14:57:01.000Z (almost 6 years ago)
• Last Synced: 2024-04-23T00:14:13.763Z (8 months ago)
• Topics: atac-seq, pipelines, single-cell
• Language: Jupyter Notebook
• Homepage:
• Size: 20.3 MB
• Stars: 35
• Watchers: 10
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-atac-analysis - Scasat (single-cell ATAC-seq analysis tool)

README

        
# Scasat (Single cell ATAC-seq analysis tool)

Scasat (single cell ATAC-seq analysis tool) is a complete pipeline to process single cell ATAC-seq data with simple steps. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. For the initial sequence processing steps, the pipeline uses a number of well-known tools which it executes from a python environment for each of the fastq files. While functions for the data analysis part are mostly written in R.

## Prerequisites:

You have to have the following tools installed in the machine where you are running Scasat

1. [samtools](http://www.htslib.org)

2. [bedtools](http://bedtools.readthedocs.io/en/latest/)

3. [macs2](https://github.com/taoliu/MACS)

4. [bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)

5. [picard](http://broadinstitute.github.io/picard/)

6. [trimmomatic](http://www.usadellab.org/cms/?page=trimmomatic)

If you have anaconda installed and using jupyter from anaconda then you can install the tools with the following anaconda commands

* samtools: conda install -c bioconda samtools

* bedtools: conda install -c bioconda bedtools 

* macs2: sudo apt install macs

* bowtie2: conda install -c bioconda bowtie2

* picard: conda install -c bioconda picard

* trimmomatic: conda install -c faircloth-lab trimmomatic

## Application: ##

Two notebooks to process two different datasets are provided here. 

### Deconvolute cell types ###

The objective of this experiment was to deconvolute the different cells from a complex mixture of cells.

__Experimental design:__

Two classic oesophageal adenocarcinoma (OAC) cell lines, OE19, OE33 and one non-neoplastic HET1A cell line were mixed together to create the complex mixture of population. These three cell lines were mixed at equal proportion to create this mixture. Single cell ATAC-seq was then performed on those two replicates by loading on two separate C1 fluidigm chips using a 96 well plate integrated fluidic circuit (IFC) and sequenced on an Illumina NextSeq. This experimental figure is shown in the figure below



### Public datatset: ###

Scasat is then applied to process and analyze the public dataset by [_Buenrostro et. al._](https://www.nature.com/articles/nature14590) and subsampled cells of [_Cusanovich et. al._](https://www.cell.com/cell/pdf/S0092-8674(18)30855-9.pdf)

## Publication

Pre-print related to Scasat can be accessed through this [link](https://www.biorxiv.org/content/early/2017/11/30/227397)