{"id":32110881,"url":"https://github.com/auroramaurizio/surfr","last_synced_at":"2026-02-19T13:32:51.031Z","repository":{"id":187219053,"uuid":"676503308","full_name":"auroramaurizio/SurfR","owner":"auroramaurizio","description":"An Rpackage to identify cells membrane marker genes from bulkRNA sequencing data","archived":false,"fork":false,"pushed_at":"2025-09-25T13:04:31.000Z","size":6933,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"devel","last_synced_at":"2026-01-29T01:38:06.639Z","etag":null,"topics":["dge","enrichment-analysis","metaanalysis","plots","proteins","public-data","rnaseq","rpackage","surface","surfaceome"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/auroramaurizio.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-08-09T10:51:35.000Z","updated_at":"2025-09-25T12:59:52.000Z","dependencies_parsed_at":"2023-10-15T13:21:54.028Z","dependency_job_id":"217ce4e7-ad4f-4ec2-b21f-9a70d1cc0cc3","html_url":"https://github.com/auroramaurizio/SurfR","commit_stats":{"total_commits":190,"total_committers":5,"mean_commits":38.0,"dds":"0.28421052631578947","last_synced_commit":"e965fd3ec11457c74e55a8863c55950fff8f6c5e"},"previous_names":["auroramaurizio/surfr"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/auroramaurizio/SurfR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/auroramaurizio%2FSurfR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/auroramaurizio%2FSurfR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/auroramaurizio%2FSurfR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/auroramaurizio%2FSurfR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/auroramaurizio","download_url":"https://codeload.github.com/auroramaurizio/SurfR/tar.gz/refs/heads/devel","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/auroramaurizio%2FSurfR/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29614953,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-19T13:04:20.082Z","status":"ssl_error","status_checked_at":"2026-02-19T13:03:33.775Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["dge","enrichment-analysis","metaanalysis","plots","proteins","public-data","rnaseq","rpackage","surface","surfaceome"],"created_at":"2025-10-20T14:05:58.026Z","updated_at":"2026-02-19T13:32:51.026Z","avatar_url":"https://github.com/auroramaurizio.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SurfR\n\nProteins at the cell surface connect intracellular and extracellular\n signaling networks and largely determine a cell’s capacity to \ncommunicate and interact with its environment. \n\nImportantly, variations in transcriptomic profiles are often observed\nbetween healthy and diseased cells, presenting distinct sets \nof cell-surface proteins. Indeed, cell surface proteins \ni) may act as biomarkers for the detection of diseased cells\n in tissues or body fluids \nand \nii) are the most prevalent target of pharmaceutical agents:\n 66% of approved human drugs listed in the DrugBank database \ntarget a cell-surface protein. \nThe investigation of the cell surfaceome therefore could \nprovide new possibilities for diagnosis, prognosis, \ntreatment development, and therapy response evaluation.\n\n\n\n## What is SurfR\n\nThe **SurfR** package aims to provide a streamlined end-to-end workflow for \nidentifying surface protein coding genes from expression data using computational prediction.\n\n\n\n**SurfR** :\n\n-   Returns a list of of surface protein coding genes, starting from \n    a list of genes of interest, the raw count matrix of your own\n    RNA-seq experiment, or from bulk transcriptomic data \n    automatically retrieved from public databases. Protein classification \n    is based on a recently developed surfaceome predictor, \n    called SURFY, based on machine learning. \n-   Allows automatic data retrieval from public databases such as \n    GEO and TCGA. GEO queries are based on the ArchS4 pipeline. \n    TCGA repository is interrogated through TCGAbiolinks.\n-   Provides a function for differential gene expression analysis. \n    For this task it relies on DESeq2 package, starting from counts data. \n-   Offers the opportunity to increase the sample size of a cohort\n    by integrating related datasets, therefore enhancing the power\n    to detect differentially expressed genes of interest. \n    Meta-analysis can be performed through metaRNASeq, taking into\n    account inter-study variability that may arise from technical\n    differences among studies (e.g., sample preparation, library\n    protocols, batch effects) as well as additional biological\n    variability.\n-   Gene ontology (GO) and pathway annotation can also be performed\n    within **SurfR** to gain further insights about surface protein\n    candidates.\n-   Includes functions to visualize DEG and enrichment results,\n    including BarPlots, Histograms, Venn diagrams, and PCA plots to help \n    users achieve efficient data interpretation.\n\n\n\n## Installation\n\nTo install this package, start R (version \"4.4\") and enter:\n\n```{r install, eval = FALSE}\n\nif (!require(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n```\n\nWhen the package is available on *Bioconductor*, use\n\n```{r install-Bioconductor, eval = FALSE}\nBiocManager::install(\"SurfR\")\n```\n\nDevelopment package version can be installed from GitHub using devtools:\n```\ndevtools::install_github(\"auroramaurizio/SurfR\")\n```\n\n\n## Dependencies\nThis package is supported for macOS, and Linux (Windows not tested). \n**SurfR** works with R v4.4 or greater.\nDependencies are indicated in the DESCRIPTION file, and can be \nautomatically installed when installing the **SurfR** pacakge. \n\n## Vignettes\n\nA comprehensive vignette provides an introduction to the **SurfR** package. \nExamples and use-cases are covered for each function.\nAdditional RMD notebooks containing the use cases code described in the manuscript are available on GitHub: https://github.com/auroramaurizio/SurfR_UseCases.\n\n## Documentation\n\nInstructions to run the main functions can be found consulting the vignette\n or by entering ?FunctionName (e.g. ?Splot) in the console after loading the package.\n\n## Citation\n\nMaurizio, A., Tascini, A.S., Morelli, M. SurfR: Riding the wave of RNA-Seq data with a comprehensive Bioconductor package to identify Surface Protein Coding Genes. Bioinformatics Advances, 2024 (DOI: 10.1093/bioadv/vbae201)\n\n## Authors\n\nAurora Maurizio (auroramaurizio1@gmail.com), \nAnna Sofia Tascini (volpesofi@gmail.com), \nMarco Morelli (morelli.marco@hsr.it)\n\n\n## Help, Suggestions, and Contributions\n\nAny contribution is highly appreciated! \nIf you are interested in contributing to this project, please open an issue.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fauroramaurizio%2Fsurfr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fauroramaurizio%2Fsurfr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fauroramaurizio%2Fsurfr/lists"}