{"id":25726563,"url":"https://github.com/jgasmits/ananseseurat","last_synced_at":"2025-05-07T08:41:35.920Z","repository":{"id":64671923,"uuid":"523760423","full_name":"JGASmits/AnanseSeurat","owner":"JGASmits","description":"Single cell ANANSE Gene-regulatory-network analysis from Seurat objects","archived":false,"fork":false,"pushed_at":"2023-11-12T12:38:00.000Z","size":635,"stargazers_count":8,"open_issues_count":2,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-28T02:05:48.222Z","etag":null,"topics":["grn-analysis","r","seurat-objects","single-cell","single-cell-atac-seq","single-cell-rna-seq"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JGASmits.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-08-11T14:45:43.000Z","updated_at":"2024-03-02T20:55:37.000Z","dependencies_parsed_at":"2023-11-11T20:26:12.556Z","dependency_job_id":"48f454d6-da6d-47e2-be59-bde365c5b9ed","html_url":"https://github.com/JGASmits/AnanseSeurat","commit_stats":{"total_commits":102,"total_committers":4,"mean_commits":25.5,"dds":"0.13725490196078427","last_synced_commit":"baa60cd21e3f3ad268e1c0310649ec8197413184"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JGASmits%2FAnanseSeurat","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JGASmits%2FAnanseSeurat/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JGASmits%2FAnanseSeurat/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JGASmits%2FAnanseSeurat/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JGASmits","download_url":"https://codeload.github.com/JGASmits/AnanseSeurat/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252844797,"owners_count":21813123,"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":["grn-analysis","r","seurat-objects","single-cell","single-cell-atac-seq","single-cell-rna-seq"],"created_at":"2025-02-25T23:17:40.252Z","updated_at":"2025-05-07T08:41:35.891Z","avatar_url":"https://github.com/JGASmits.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/\",\n  out.width = \"100%\"\n)\n```\n# `AnanseSeurat` package \u003cimg src='man/figures/logo.png' align=\"right\" height=\"139\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/JGASmits/AnanseSeurat/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/JGASmits/AnanseSeurat/actions/workflows/R-CMD-check.yaml)\n[![codecov](https://codecov.io/github/JGASmits/AnanseSeurat/branch/main/graph/badge.svg?token=0XKWAD0KJ7)](https://codecov.io/github/JGASmits/AnanseSeurat)\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/AnanseSeurat)](https://cran.r-project.org/package=AnanseSeurat)\n\u003c!-- badges: end --\u003e\n\nThe `AnanseSeurat` package takes pre-processed clustered single cell objects of scRNAseq and scATACseq or a multiome combination, and generates files for gene regulatory network (GRN) analysis.\n\n\n## Installation\n\n `AnanseSeurat` can be installed using\n```{r, eval=FALSE}\nlibrary(devtools) # Tools to Make Developing R Packages Easier # Tools to Make Developing R Packages Easier\nSys.unsetenv(\"GITHUB_PAT\")\nremotes::install_github(\"JGASmits/AnanseSeurat@main\")\n```\n\n### Usage\n```{r eval=FALSE}\nlibrary(\"AnanseSeurat\")\nrds_file \u003c- './scANANSE/preprocessed_PDMC.Rds'\npbmc \u003c- readRDS(rds_file)\n```\n\nNext you can output the data from your single cell object, the file format, config file and sample file are all ready to automate  GRN analysis using `anansnake`.\nhttps://github.com/vanheeringen-lab/anansnake\n\n```{r, eval=FALSE}\nexport_CPM_scANANSE(\n  pbmc,\n  min_cells = 25,\n  output_dir = './scANANSE/analysis',\n  cluster_id = 'predicted.id',\n  RNA_count_assay = 'RNA'\n)\n\nexport_ATAC_scANANSE(\n  pbmc,\n  min_cells = 25,\n  output_dir = './scANANSE/analysis',\n  cluster_id = 'predicted.id',\n  ATAC_peak_assay = 'peaks'\n)\n\n# Specify additional contrasts:\ncontrasts \u003c-  c('B-naive_B-memory',\n                'B-memory_B-naive',\n                'B-naive_CD14-Mono',\n                'CD14-Mono_B-naive')\n\nconfig_scANANSE(\n  pbmc,\n  min_cells = 25,\n  output_dir = './scANANSE/analysis',\n  cluster_id = 'predicted.id',\n  additional_contrasts = contrasts\n)\n\nDEGS_scANANSE(\n  pbmc,\n  min_cells = 25,\n  output_dir = './scANANSE/analysis',\n  cluster_id = 'predicted.id',\n  additional_contrasts = contrasts\n)\n```\n\n\n### install and run anansnake \n\nFollow the instructions its respective github page, https://github.com/vanheeringen-lab/anansnake\nAfter activating the conda environment, use the generated files to run GRN analysis using your single cell cluster data:\n\n```{bash eval=FALSE}\nanansnake \\\n--configfile scANANSE/analysis/config.yaml \\\n--resources mem_mb=48_000 --cores 12\n```\n\n\n\n### import ANANSE results back to your single cell object\nAfter running Anansnake, you can import the TF influence scores back into your single cell object of choice\n```{r eval=FALSE}\npbmc \u003c- import_seurat_scANANSE(pbmc,\n                               cluster_id = 'predicted.id',\n                               anansnake_inf_dir = \"./scANANSE/analysis/influence\")\nTF_influence \u003c- per_cluster_df(pbmc,\n                               cluster_id = 'predicted.id',\n                               assay = 'influence')\n```\n\n\n### Thanks to:\n\n* Julian A. Arts and his Pycharm equivalent of this package https://github.com/Arts-of-coding/AnanseScanpy \n* Siebren Frohlich and his anansnake implementation https://github.com/vanheeringen-lab/anansnake\n* Rebecca R. Snabel for her implementation of the motif expression correlation analysis\n* Branco Heuts for testing\n\n# Credits\nThe hex sticker is generated using the [```hexSticker```](https://github.com/GuangchuangYu/hexSticker) package.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjgasmits%2Fananseseurat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjgasmits%2Fananseseurat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjgasmits%2Fananseseurat/lists"}