{"id":15554908,"url":"https://github.com/fpaskali/lfapp","last_synced_at":"2026-02-25T16:34:24.145Z","repository":{"id":44053205,"uuid":"347970850","full_name":"fpaskali/LFApp","owner":"fpaskali","description":"The repository includes the development version of R package LFApp","archived":false,"fork":false,"pushed_at":"2024-05-27T13:56:04.000Z","size":14664,"stargazers_count":3,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-12T05:18:15.974Z","etag":null,"topics":["calibration","image-processing","medical-image-processing","r","r-package","rstats","shiny"],"latest_commit_sha":null,"homepage":"","language":"R","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/fpaskali.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-03-15T13:00:40.000Z","updated_at":"2024-05-27T13:56:08.000Z","dependencies_parsed_at":"2023-01-24T18:04:42.787Z","dependency_job_id":"2f49654d-924e-47f8-b5e4-5306b5c1ce52","html_url":"https://github.com/fpaskali/LFApp","commit_stats":{"total_commits":174,"total_committers":4,"mean_commits":43.5,"dds":"0.37356321839080464","last_synced_commit":"cb565f73f9cb681c031265c59a6b507a89c47b06"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fpaskali%2FLFApp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fpaskali%2FLFApp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fpaskali%2FLFApp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fpaskali%2FLFApp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fpaskali","download_url":"https://codeload.github.com/fpaskali/LFApp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250506996,"owners_count":21441895,"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":["calibration","image-processing","medical-image-processing","r","r-package","rstats","shiny"],"created_at":"2024-10-02T15:04:23.141Z","updated_at":"2026-02-25T16:34:24.100Z","avatar_url":"https://github.com/fpaskali.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# \u003cimg src=\"https://github.com/fpaskali/LFApp/blob/main/hex-LFApp.png\" alt=\"LFApp\" width=\"120\"/\u003e \u0026emsp; Shiny Apps for Lateral Flow Assays\n\nThe repository includes the development version of R package LFApp\n\n[![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg)](https://www.gnu.org/licenses/lgpl-3.0)\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n\n\n## Description\n\nThe LFA shiny apps include in package LFApp consist of four modular Shiny \napplications: \n\n(1) **LFA App core** for image acquisition, editing, region of interest definition \nvia gridding, background correction with multiple available methods, as well as \nintensity data extraction of the pre-defined bands of the analysed LFAs. More\nprecisely, it consists of Tab 1, Tab 2 and parts of Tab 3 described in detail \nbelow.\n\n(2) **LFA App calibration** extends the LFA App core by methods for merging the\nintensity data with information from experiments, computation of calibration \nmodels and the generation of a report about the calibration analysis. The \nfunctionality corresponds to the Tabs 1-6 described below.\n\n(3) **LFA App quantification** enables quantification of the extracted intensity \nvalues via loading existing calibration models. It extends the LFA App core\nby Tab 7 described below.\n\n(4) **LFA App analysis** includes the full functionality mentioned above and \nenables full analysis in one application. That is, it consists of Tab 1-7.\n\n\nThe graphical user interface of the apps is built in a modular way divided into \nseveral tabs, where each tab represents a specific step of the workflow. While \nthe applications can be used in a sequential fashion, the specific steps can \nalso be carried out individually. \n\n![LFApp](LFAppMain.png)\n\n\n## Testing our apps: shinyapps.io\n\nOur apps can also be tested on shinyapps.io. The desktop version of our full\npurpose analysis app is at\n\nhttps://lfapp.shinyapps.io/LFAnalysis/\n\nThe mobile version is at\n\nhttps://lfapp.shinyapps.io/mobile_app/\n\n\n## Installation\n\nThe package requires Bioconductor package EBImage, which should be installed\nfirst via\n\n```{r}\n## Install package BiocManager\nif(!requireNamespace(\"BiocManager\", quietly = TRUE)) \n  install.packages(\"BiocManager\")\n## Use BiocManager to install EBImage\nBiocManager::install(\"EBImage\", update = FALSE)\n```\n\nFor generating our vignette and automatic reports, we need packages knitr and\nrmarkdown, which will be installed next.\n\n```{r}\n## Install package knitr\nif(!requireNamespace(\"knitr\", quietly = TRUE)) \n  install.packages(\"knitr\")\n## Install package rmarkdown\nif(!requireNamespace(\"rmarkdown\", quietly = TRUE)) \n  install.packages(\"rmarkdown\")\n```\n\nFinally, one can install package LFApp, where all remaining dependencies will\nbe installed automatically.\n\n```{r}\n## Install package LFApp\nremotes::install_github(\"fpaskali/LFApp\", build_vignette=TRUE)\n```\n\n## Start Apps\nLFApp consist of four different shiny apps where there is a desktop and a \nmobile version for each app. They can be started with one of the following \ncommands: \n\n```{r}\n## desktop versions\n## LFA App core\nLFApp::run_core()\n\n## LFA App quantification\nLFApp::run_quan()\n\n## LFA App calibration\nLFApp::run_cal()\n\n## LFA App full analysis\nLFApp::run_analysis()\n\n## mobile versions\n## LFA App core\nLFApp::run_mobile_core()\n\n## LFA App quantification\nLFApp::run_mobile_quan()\n\n## LFA App calibration\nLFApp::run_mobile_cal()\n\n## LFA App full analysis\nLFApp::run_mobile_analysis()\n```\n\n## Open User's Guide\n\nA comprehensive user's guide is included in our package in form of a so-called\nvignette and can be opened via\n\n```{r}\nvignette(\"LFApp\")\n```\n\nYou can also find it at https://fpaskali.github.io/LFApp/\n\n\n## YouTube Videos\n\nThere is a playlist at https://www.youtube.com/playlist?list=PLRgOZXM8LZ0gJwtsFNxBiu9WJG1TJjFuP\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffpaskali%2Flfapp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffpaskali%2Flfapp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffpaskali%2Flfapp/lists"}