{"id":20837738,"url":"https://github.com/astrazeneca/omicsfold","last_synced_at":"2025-05-08T20:29:32.137Z","repository":{"id":42448462,"uuid":"330646423","full_name":"AstraZeneca/Omicsfold","owner":"AstraZeneca","description":"Multi-omics data normalisation, model fitting and visualisation.","archived":false,"fork":false,"pushed_at":"2023-04-06T15:01:06.000Z","size":502,"stargazers_count":24,"open_issues_count":1,"forks_count":7,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-31T17:59:01.545Z","etag":null,"topics":[],"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/AstraZeneca.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":"AUTHORS.md","dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-01-18T11:34:34.000Z","updated_at":"2024-10-06T23:43:05.000Z","dependencies_parsed_at":"2024-09-26T02:01:31.739Z","dependency_job_id":null,"html_url":"https://github.com/AstraZeneca/Omicsfold","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2FOmicsfold","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2FOmicsfold/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2FOmicsfold/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2FOmicsfold/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AstraZeneca","download_url":"https://codeload.github.com/AstraZeneca/Omicsfold/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253144206,"owners_count":21861016,"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":"2024-11-18T01:08:25.843Z","updated_at":"2025-05-08T20:29:32.120Z","avatar_url":"https://github.com/AstraZeneca.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Omicsfold\n\n![Maturity level-Prototype](https://img.shields.io/badge/Maturity%20Level-Prototype-red)\n\n![](omicsfold_id.png)\n\n### Multi-omics data normalisation, model fitting, and visualisation.\n\n## Overview\n\nThis is a utility R package containing custom code and scripts developed to\nestablish a working approach for integration of multi-omics data.\n\nThe package provides a unified toolkit for the analysis and integration of\nmulti-omic high-throughput data. It relies upon the\n[`mixOmics`](http://mixomics.org/) toolkit to provide implementations of many of\nthe underlying projection to latent structures (PLS) methods used to analyse\nhigh-dimensional data. In addition to this, it includes custom implementations\nof data pre-processing, normalisation, collation, model validation,\nvisualisation \u0026 output functions.\n\nThe originally individual scripts have been collected into a formal package that\nshould be installable and usable within an analysts' R environment without\nfurther configuration. The package is fully documented at the function level.\n\n## Getting Started\n\nThis package and analysis requires R v3.6 or above. It is largely built upon the\n`mixOmics` integration framework. The dependencies vary significantly in source,\nso an installation script is provided to make satisfying the dependencies as\nsimple as possible. `mixOmics` installs its own dependencies as well. Note that\nwe install `mixOmics` from the GitHub repository as this version is more up to\ndate than the one on Bioconductor and has a number of fixes which are needed to\navoid bugs.\n\nNotable dependencies that will be installed if they are not already:\n\n- mixOmics\n- WGCNA\n- ggplot2\n- dplyr \u0026 magrittr\n- reshape2\n\nSee the [`DESCRIPTION`](OmicsFold/DESCRIPTION) file for a complete\ndependency list\n\n### Installation\n\nDue to the number of dependencies and the number of places those dependencies\ncome from, there is an installation script available.  This can be run by\nopening up an R session in your preferred environment, ensuring your working\ndirectory is the `OmicsFold` directory, then issuing the following commands:\n\n```R\nsource('install.R')\ninstall.omicsfold()\n```\n\nThis should install all the dependencies and then finally the OmicsFold package\nitself.  If there are any issues due to versions changing or changes in which\nrepository maintains the active version of a package, you may have to update the\nscript.\n\nIf you are having issues installing OmicsFold in a conda environment, please try\nthe following steps: \n\nFirst, create the conda environment:\n```Shell\nconda create --name OmicsFold \nsource activate OmicsFold\nconda install r=3.6.0\nconda install -c conda-forge boost-cpp\n```\n\nSecond, launch R in the conda environment and manually install the following packages (or if you are installing directly in a local instance of R):\n```R\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\nBiocManager::install(\"metagenomeSeq\")\nBiocManager::install(\"org.Mm.eg.db\")\ninstall.packages(\"XML\", repos = \"http://www.omegahat.net/R\")\nsource(\"http://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/GeneAnnotation/installAnRichment.R\")\ninstallAnRichment()\nsource('install.R')\ninstall.omicsfold()\n```\nFor installation using nextflow (https://www.nextflow.io/docs/latest/getstarted.html) please see https://github.com/AstraZeneca/Omicsfold/tree/master/OmicsFold/nextflow_pipeline\n\n### Usage\n\nImport the `OmicsFold` and the `mixOmics` packages in R and you're ready to\ngo.  Some functions also require `dplyr` to be loaded so it's a good idea to\nload it anyway. Certain plotting functions also may require ggplot2 to be loaded.\n\n```R\nlibrary(OmicsFold)\nlibrary(mixOmics)\nlibrary(dplyr)\nlibrary (ggplot2) #(optional)\n```\n\n### Data Normalisation\n\nA number of normalisation functions have been provided.  Each has documentation\nwhich can be read in the usual way in R.  For example, the help for the function\n`normalise.tss` can be viewed by calling `?normalise.tss`.  A brief description\nof the usage of each function can be read in the [Getting Started with\nNormalisation](docs/getting-started-normalisation.md) document, with a few key\nfunctions also showing example code for how to use it.\n\n- `low.count.removal()`\n- `normalise.tss()`\n- `normalise.css()`\n- `normalise.logit()`\n- `normalise.logit.empirical()`\n- `normalise.clr()`\n- `normalise.clr.within.features()`\n\n### Analysis of mixOmics Output\n\nOnce a `mixOmics` model has been fitted, OmicsFold can be used to perform a\nnumber of visualisation and data extraction functions.  Below is a brief list of\nthe functionality provided.  While these are well documented in the R help\nsystem, descriptions of how to use each function can also be found in the\n[Getting Started with Model Analysis](docs/getting-started-model-analysis.md)\ndocument.\n\n- **Model variance analysis** - functions are provided to extract the percentage\n  contributions of each component to the model variance and the centroids of\n  variance across the blocks of a DIABLO model.\n- **Feature analysis for sPLS-DA models** - feature loadings on the fitted\n  singleomics model can be exported as a sorted table, while feature stability\n  across many sparse model fits can also be exported.  As there may be many\n  components to export stability for, another function lets you combine these\n  into a single table as well as a plotting function allowing you to plot\n  stability of the selected features as a visualisation.\n- **Feature analysis for DIABLO models** - similarly to the features for\n  singleomics models above, multiomics models can also have feature loadings and\n  stability exported. Associated correlations between features of different \n  blocks can be exported as either a matrix and then also converted to a CSV \n  file appropriate for importing into Cytoscape where it can form a network \n  graph.\n- **Model predictivity** - we provide a function to plot the predictivity of a\n  model from a confusion matrix.\n- **Utility functions** - offers a way to take long feature names being passed\n  to plots and truncate them for display.\n- **BlockRank** - implements a novel approach to analysing feature importance \n  between blocks of data.\n\n\n\n## Other Information\n\nTo contact the maintainers or project director, please refer to the\n[`AUTHORS`](AUTHORS.md) file.  If you are thinking of contributing to OmicsFold,\nall the information you will need is in the [`CONTRIBUTING`](CONTRIBUTING.md)\nfile.\n\nOmicsFold is licensed under the [Apache-2.0 software\nlicence](https://www.apache.org/licenses/LICENSE-2.0) as documented in the\n[`LICENCE`](LICENCE.md) file.  Separately installed dependencies of OmicsFold\nmay be licensed under different licence agreements.  If you plan to create\nderivative works from OmicsFold or use OmicsFold for commercial or profitable\nenterprises, please ensure you adhere to all the expectations of these\ndependencies and seek legal advice if you are unsure.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fomicsfold","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fastrazeneca%2Fomicsfold","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fomicsfold/lists"}