{"id":13763658,"url":"https://github.com/ManchesterBioinference/Scasat","last_synced_at":"2025-05-10T17:30:55.668Z","repository":{"id":90882692,"uuid":"112540072","full_name":"ManchesterBioinference/Scasat","owner":"ManchesterBioinference","description":"Scasat is a single cell ATAC-seq preprocessing and analysis pipeline","archived":false,"fork":false,"pushed_at":"2019-02-27T14:57:01.000Z","size":21243,"stargazers_count":35,"open_issues_count":0,"forks_count":6,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-04-23T00:14:13.763Z","etag":null,"topics":["atac-seq","pipelines","single-cell"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ManchesterBioinference.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-11-29T23:33:53.000Z","updated_at":"2023-12-06T09:07:43.000Z","dependencies_parsed_at":null,"dependency_job_id":"78c9e898-9716-42ce-89e9-b3a9e6b29175","html_url":"https://github.com/ManchesterBioinference/Scasat","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManchesterBioinference%2FScasat","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManchesterBioinference%2FScasat/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManchesterBioinference%2FScasat/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManchesterBioinference%2FScasat/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ManchesterBioinference","download_url":"https://codeload.github.com/ManchesterBioinference/Scasat/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253453199,"owners_count":21911056,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["atac-seq","pipelines","single-cell"],"created_at":"2024-08-03T15:00:54.895Z","updated_at":"2025-05-10T17:30:51.659Z","avatar_url":"https://github.com/ManchesterBioinference.png","language":"Jupyter Notebook","funding_links":[],"categories":["Single-cell"],"sub_categories":[],"readme":"# Scasat (Single cell ATAC-seq analysis tool)\nScasat (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.\n\n## Prerequisites:\n\nYou have to have the following tools installed in the machine where you are running Scasat\n1. [samtools](http://www.htslib.org)\n2. [bedtools](http://bedtools.readthedocs.io/en/latest/)\n3. [macs2](https://github.com/taoliu/MACS)\n4. [bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)\n5. [picard](http://broadinstitute.github.io/picard/)\n6. [trimmomatic](http://www.usadellab.org/cms/?page=trimmomatic)\n\nIf you have anaconda installed and using jupyter from anaconda then you can install the tools with the following anaconda commands\n* samtools: conda install -c bioconda samtools\n* bedtools: conda install -c bioconda bedtools \n* macs2: sudo apt install macs\n* bowtie2: conda install -c bioconda bowtie2\n* picard: conda install -c bioconda picard\n* trimmomatic: conda install -c faircloth-lab trimmomatic\n\n\n\n## Application: ##\nTwo notebooks to process two different datasets are provided here. \n\n### Deconvolute cell types ###\n\nThe objective of this experiment was to deconvolute the different cells from a complex mixture of cells.\n\n__Experimental design:__\n\nTwo 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\n\n\u003cimg src=\"ExperimentalDesign.png\" alt=\"Experimental Desing\" style=\"width: 500px;\"/\u003e\n\n### Public datatset: ###\nScasat 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)\n\n## Publication\nPre-print related to Scasat can be accessed through this [link](https://www.biorxiv.org/content/early/2017/11/30/227397)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FManchesterBioinference%2FScasat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FManchesterBioinference%2FScasat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FManchesterBioinference%2FScasat/lists"}