{"id":13592572,"url":"https://github.com/kieranrcampbell/ouija","last_synced_at":"2025-12-25T11:06:33.540Z","repository":{"id":71097821,"uuid":"55062146","full_name":"kieranrcampbell/ouija","owner":"kieranrcampbell","description":"Descriptive probabilistic marker gene approach to single-cell pseudotime inference","archived":false,"fork":false,"pushed_at":"2019-11-29T16:11:10.000Z","size":9492,"stargazers_count":28,"open_issues_count":9,"forks_count":3,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-08-02T16:45:15.940Z","etag":null,"topics":["bayesian-methods","factor-analysis","gene-expression","single-cell"],"latest_commit_sha":null,"homepage":"http://kieranrcampbell.github.io/ouija","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/kieranrcampbell.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-03-30T12:44:36.000Z","updated_at":"2022-01-15T22:57:56.000Z","dependencies_parsed_at":"2023-06-05T21:30:44.594Z","dependency_job_id":null,"html_url":"https://github.com/kieranrcampbell/ouija","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/kieranrcampbell%2Fouija","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kieranrcampbell%2Fouija/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kieranrcampbell%2Fouija/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kieranrcampbell%2Fouija/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kieranrcampbell","download_url":"https://codeload.github.com/kieranrcampbell/ouija/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223346845,"owners_count":17130517,"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":["bayesian-methods","factor-analysis","gene-expression","single-cell"],"created_at":"2024-08-01T16:01:10.889Z","updated_at":"2025-12-25T11:06:28.486Z","avatar_url":"https://github.com/kieranrcampbell.png","language":"R","funding_links":[],"categories":["Software packages"],"sub_categories":["RNA-seq","Pseudotime and trajectory inference"],"readme":"# Ouija\n\n\nOuija is a probabilistic pseudotime framework. Ouija \n\n* infers pseudotimes from a **small number of marker genes** letting you understand **why** the pseudotimes have been learned in terms of those genes (**A**)\n* provides parameter estimates (with uncertainty) for **interpretable gene regulation behaviour** (such as the peak time or the upregulation time) (**B**)\n* has a Bayesian hypothesis test to **find genes regulated before others** along the trajectory (**C**)\n* identifies **metastable states**, ie discrete cell types along the continuous trajectory (**D**)\n\n\u003cimg src=\"inst/www/fig_main.png\" width=\"600\"/\u003e\n\n\n## Getting started\n\n### Installation\n\n```r\n# install.packages(\"devtools\")\ndevtools::install_github(\"kieranrcampbell/ouija\")\n```\n\nTo build the Ouija vignette install using\n\n```r\ndevtools::install_github(\"kieranrcampbell/ouija\", local = FALSE, \n                          args = \"--preclean\", build_vignettes = TRUE)\n```\n\n### Model fitting\n\nInput is a cell-by-gene expression matrices that is non-negative and represents logged gene expression values. We recommend using `log2(TPM + 1)`. This can either take the form of a matrix or a [`SingleCellExperiment`](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) (use of the `SingleCellExperiment` infrastructure is highly encouraged for single-cell analyses). By default the `logcounts` assay of a `SingleCellExperiment` will be used.\n\nTo fit the pseudotimes, pass the input data to the `ouija` function:\n\n```r\nlibrary(ouija)\ndata(example_gex) # synthetic gene expression data bundled\noui \u003c- ouija(example_gex)\npseudotimes \u003c- map_pseudotime(oui)\n```\n\nThe `map_pseudotimes` function extracts the maximum-a-posteriori (MAP) estimates of the pseudotimes.\n\nFor further usage options see the vignette. A prebuilt vignette can be found [here](http://kieranrcampbell.github.io/ouija).\n\n\n## Authors\n\nKieran Campbell \u0026 Christopher Yau  \nWellcome Trust Centre for Human Genetics, University of Oxford\n\n## Artwork\n\n\u003cimg src=\"inst/www/chris_ouija.jpg\" width=\"500\"/\u003e\n\nArtwork by `cwcyau`, the mysterious banksy-esque artist of the statistical genomics world.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkieranrcampbell%2Fouija","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkieranrcampbell%2Fouija","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkieranrcampbell%2Fouija/lists"}