{"id":13612040,"url":"https://github.com/NKI-CCB/won-parafac","last_synced_at":"2025-04-13T11:31:31.550Z","repository":{"id":127720272,"uuid":"207774861","full_name":"NKI-CCB/won-parafac","owner":"NKI-CCB","description":"Weighted orthogonal non-negative (WON) parallel factor analsyis (PARAFAC)","archived":false,"fork":false,"pushed_at":"2021-05-17T15:41:28.000Z","size":14452,"stargazers_count":1,"open_issues_count":1,"forks_count":2,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-11-07T20:41:37.606Z","etag":null,"topics":["bioinformatics","genomics","matlab","parafac","unsupervised-learning"],"latest_commit_sha":null,"homepage":null,"language":"MATLAB","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/NKI-CCB.png","metadata":{"files":{"readme":"README.md","changelog":null,"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}},"created_at":"2019-09-11T09:31:52.000Z","updated_at":"2021-05-17T15:41:30.000Z","dependencies_parsed_at":null,"dependency_job_id":"fdc772b4-b0c7-4c96-8937-d174eacef55e","html_url":"https://github.com/NKI-CCB/won-parafac","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/NKI-CCB%2Fwon-parafac","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NKI-CCB%2Fwon-parafac/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NKI-CCB%2Fwon-parafac/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NKI-CCB%2Fwon-parafac/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NKI-CCB","download_url":"https://codeload.github.com/NKI-CCB/won-parafac/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248705661,"owners_count":21148571,"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":["bioinformatics","genomics","matlab","parafac","unsupervised-learning"],"created_at":"2024-08-01T20:00:21.508Z","updated_at":"2025-04-13T11:31:30.534Z","avatar_url":"https://github.com/NKI-CCB.png","language":"MATLAB","funding_links":[],"categories":["Software packages and methods"],"sub_categories":["Multi-omics correlation or factor analysis"],"readme":"# WON-PARAFAC\n\n### Weighted orthogonal non-negative (WON) parallel factor analsyis (PARAFAC)\n\nWON-PARAFAC is a variant of parallel factor analysis (PARAFAC), a tensor factorization method.\nWON-PARAFAC impose the following three constraints on the standard PARAFAC:\n1. Weighting scheme\n- For balanced integration of the multiple data types\n2. Orthogonality constraint\n- To reduce overlapping between a factor (originally used on gene mode). This also introduces extra sparcity on the mode.\n3. Non-negativity\n- To induce sparse and parts-based representation.\n\n### Implementation / Dependency\n\nA multiplicative update rule was used to derive the algorithm, as in the original NMF implementation from [Lee \u0026 Seung (Nature, 1999)](https://www.nature.com/articles/44565).\nThe code requires [tensor toolbox version 2.6 (by Tamara Kolda)](https://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html\n), freely available for non-commercial use upon registration.\n\nFor running the code, tenstor toobox must be avilable on the path environment, using `addpath` command in MATLAB.\n\n### Demo code and data\n\nYou can load demo data, which contains pan-cancer multiomics data produced in [GDSC1000 project (Sanger)](https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html).\nYou can load the data by:\n\n```matlab\nload Demo.mat\n```\nThe command will load a varialbe `X`, a 3-way tensor (1815 gene by 935 cell lines and 5 data types).\nNote that the 5 data types corresponds to below:\n- positive gene expression levels (non-negative continuous; GE(+))\n- absolute value of negative gene expression levels (non-negative continuous; GE(-))\n- mutation (binary; MT)\n- copy number gain (binary; CN(+))\n- copy number loss (binary; CN(-))\n\nThe list of genes names in `X` is indicated in `genenames`, which will also be loaded together with `X`.\n\n`Demo.m` will perform WON-PARAFAC analysis using random 100 genes by default, and varying number of factors and strength of orthogonal constraint on gene factor matrix.\n\n- Number of basis: 10, 20, 30, ..., 200\n- Strength of orthogonal constraint: 0 (no constraint), 0.2, 0.5, 1\n\nFinally, a plot will be generated to show the performance of WON-PARAFAC for reconstructing input tensor (see below for an example).\n\n![alt text](https://github.com/NKI-CCB/won-parafac/blob/master/Demo_plot.png \"Demo plot\")\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNKI-CCB%2Fwon-parafac","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNKI-CCB%2Fwon-parafac","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNKI-CCB%2Fwon-parafac/lists"}