{"id":13681107,"url":"https://github.com/OKdll/mpwR","last_synced_at":"2025-04-30T03:30:41.859Z","repository":{"id":38352568,"uuid":"504159647","full_name":"OKdll/mpwR","owner":"OKdll","description":"Compare workflows in mass spectrometry based bottom-up proteomics.","archived":false,"fork":false,"pushed_at":"2024-09-09T14:29:12.000Z","size":9884,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-12T00:36:23.340Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://okdll.github.io/mpwR/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OKdll.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","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}},"created_at":"2022-06-16T13:07:39.000Z","updated_at":"2024-08-26T14:57:46.000Z","dependencies_parsed_at":"2023-11-13T22:38:36.366Z","dependency_job_id":null,"html_url":"https://github.com/OKdll/mpwR","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/OKdll%2FmpwR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OKdll%2FmpwR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OKdll%2FmpwR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OKdll%2FmpwR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OKdll","download_url":"https://codeload.github.com/OKdll/mpwR/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251635007,"owners_count":21619125,"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":[],"created_at":"2024-08-02T13:01:26.472Z","updated_at":"2025-04-30T03:30:36.849Z","avatar_url":"https://github.com/OKdll.png","language":"R","readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"80%\"\n)\n```\n\n# mpwR \u003cimg src='man/figures/mpwR_hexagon.png' align=\"right\" width=\"15%\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/OKdll/mpwR/workflows/R-CMD-check/badge.svg)](https://github.com/OKdll/mpwR/actions)\n[![CRAN status](https://www.r-pkg.org/badges/version/mpwR)](https://CRAN.R-project.org/package=mpwR)\n\u003c!-- badges: end --\u003e\n\nmpwR [ɪmˈpaʊə(r)] offers a systematic approach for comparing proteomic workflows and empowers the researcher to effortlessly access valuable information about identifications, data completeness, quantitative precision, and other performance indicators across an unlimited number of analyses and multiple software tools. It can be used to analyze label-free mass spectrometry-based experiments with data-dependent or data-independent spectral acquisition.\n\n## Applications - RMarkdown or Shiny\nThe functions of mpwR provide a great foundation to generate customized reports e.g. with RMarkdown or to build shiny apps/dashboards for downstream data analysis. An example for a shiny dashboard is also available - you can access the dashboard [here](https://okdll.shinyapps.io/mpwR/). \n\n## Installation\n\nInstall the development version from [GitHub](https://github.com/OKdll/mpwR) using the [`devtools`](https://github.com/r-lib/devtools) package by using the following commands:\n\n```{r, eval = FALSE}\n# install.packages(\"devtools\") #remove \"#\" if you do not have devtools package installed yet\ndevtools::install_github(\"OKdll/mpwR\", dependencies = TRUE) # use dependencies TRUE to install all required packages for mpwR\n```\n\n## Preparation\n\n### Requirements \nAs input the standard outputs of ProteomeDiscoverer, Spectronaut, DIA-NN or MaxQuant are supported by mpwR. Details about further requirements are listed in the vignette [Requirements](https://okdll.github.io/mpwR/articles/Requirements.html).\n\n### Import \nImporting the output files from each software can be easily performed with `prepare_mpwR`. Further details about importing data are highlighted in the vignette [Import](https://okdll.github.io/mpwR/articles/Import.html).\n\n```{r import, eval = FALSE}\nfiles \u003c- prepare_mpwR(path = \"Path_to_Folder_with_files\")\n```\n\n### Load packages\n```{r libraries, message=FALSE, warning=FALSE}\nlibrary(mpwR)\nlibrary(flowTraceR)\nlibrary(magrittr) \nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(stringr)\nlibrary(tibble)\nlibrary(ggplot2)\n```\n\n## Example - Workflow\n\nThis is a basic example which shows the downstream analysis for number of identifications and data completeness. Please check the vignette [Workflow](https://okdll.github.io/mpwR/articles/Workflow.html) for a detailed analysis pipeline and more functionalities.\n\n```{r example}\n#get example\nfiles \u003c- create_example()\n```\n\n# Number of Identifications\n\n## Report\nThe number of identifications can be determined with `get_ID_Report`. \n```{r ID-Report}\nID_Reports \u003c- get_ID_Report(input_list = files)\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\nFor each analysis an ID Report is generated and stored in a list. Each ID Report entry can be easily accessed:\n```{r show-ID-Report}\nID_Reports[[\"DIA-NN\"]]\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n## Plot\n\n### Individual\nEach ID Report can be plotted with `plot_ID_barplot` from precursor- to proteingroup-level. The generated barplots are stored in a list.\n```{r plot-ID-barplot}\nID_Barplots \u003c- plot_ID_barplot(input_list = ID_Reports, level = \"ProteinGroup.IDs\")\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\nThe individual barplots can be easily accessed:\n```{r show-ID-barplot}\nID_Barplots[[\"DIA-NN\"]]\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n### Summary\nAs a visual summary a boxplot can be generated with `plot_ID_boxplot`.\n```{r plot-ID-boxplot}\nplot_ID_boxplot(input_list = ID_Reports, level = \"ProteinGroup.IDs\")\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n# Data Completeness\n\n## Report\nData Completeness can be determined with `get_DC_Report` for absolute numbers or in percentage. \n\n```{r DC-Report}\nDC_Reports \u003c- get_DC_Report(input_list = files, metric = \"absolute\")\nDC_Reports_perc \u003c- get_DC_Report(input_list = files, metric = \"percentage\")\n``` \n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\nFor each analysis a DC Report is generated and stored in a list. Each DC Report entry can be easily accessed:\n```{r show-DC-Report}\nDC_Reports[[\"DIA-NN\"]]\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n## Plot\n\n### Individual\n#### Absolute\nEach DC Report can be plotted with `plot_DC_barplot` from precursor- to proteingroup-level. The generated barplots are stored in a list.\n```{r plot-DC-barplot}\nDC_Barplots \u003c- plot_DC_barplot(input_list = DC_Reports, level = \"ProteinGroup.IDs\", label = \"absolute\")\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\nThe individual barplots can be easily accessed:\n```{r show-DC-barplot}\nDC_Barplots[[\"DIA-NN\"]]\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n#### Percentage\n```{r show-DC-barplot-percentage}\nplot_DC_barplot(input_list = DC_Reports_perc, level = \"ProteinGroup.IDs\", label = \"percentage\")[[\"DIA-NN\"]]\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n### Summary\nAs a visual summary a stacked barplot can be generated with `plot_DC_stacked_barplot`.\n\n#### Absolute\n```{r plot-DC-stacked-barplot}\nplot_DC_stacked_barplot(input_list = DC_Reports, level = \"ProteinGroup.IDs\", label = \"absolute\")\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n#### Percentage\n```{r plot-DC-stacked-barplot-percentage}\nplot_DC_stacked_barplot(input_list = DC_Reports_perc, level = \"ProteinGroup.IDs\", label = \"percentage\")\n```\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\n","funding_links":[],"categories":["5. Raw Data Analysis"],"sub_categories":["Table of Contents"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOKdll%2FmpwR","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FOKdll%2FmpwR","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOKdll%2FmpwR/lists"}