{"id":13612048,"url":"https://github.com/shu-hai/D-CCA","last_synced_at":"2025-04-13T11:31:28.959Z","repository":{"id":202124376,"uuid":"164795883","full_name":"shu-hai/D-CCA","owner":"shu-hai","description":"A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)","archived":false,"fork":false,"pushed_at":"2022-08-10T04:45:26.000Z","size":3887,"stargazers_count":11,"open_issues_count":0,"forks_count":10,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-07T20:41:30.651Z","etag":null,"topics":["data-fusion","data-integration","high-dimensional-data","integrative-analysis","multiblock-structures","multiview"],"latest_commit_sha":null,"homepage":"","language":"Python","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/shu-hai.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}},"created_at":"2019-01-09T05:41:58.000Z","updated_at":"2024-11-05T16:17:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"415563f5-2b79-4e5c-81fe-5ed376041596","html_url":"https://github.com/shu-hai/D-CCA","commit_stats":null,"previous_names":["shu-hai/d-cca"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shu-hai%2FD-CCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shu-hai%2FD-CCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shu-hai%2FD-CCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shu-hai%2FD-CCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shu-hai","download_url":"https://codeload.github.com/shu-hai/D-CCA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248705648,"owners_count":21148568,"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":["data-fusion","data-integration","high-dimensional-data","integrative-analysis","multiblock-structures","multiview"],"created_at":"2024-08-01T20:00:21.654Z","updated_at":"2025-04-13T11:31:28.021Z","avatar_url":"https://github.com/shu-hai.png","language":"Python","funding_links":[],"categories":["Software packages and methods"],"sub_categories":["Multi-omics correlation or factor analysis"],"readme":"# D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets\nThis python package implements the D-CCA method proposed in [1] for K=2 datasets. See [example.py](https://github.com/shu-hai/D-CCA/blob/master/example.py) for details, with Python 3.5 or above. For K\u003e2 datasets, please use the [D-GCCA](https://github.com/shu-hai/D-GCCA) method.\n\n\nD-CCA conducts the following decomposition:\n\n\u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{Y}_k=\\boldsymbol{X}_k %2B \\boldsymbol{E}_k=\\boldsymbol{C}_k %2B \\boldsymbol{D}_k %2B \\boldsymbol{E}_k\"\u003e for \u003cimg src=\"https://render.githubusercontent.com/render/math?math=k=1,2\"\u003e\n\nwhere \u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{C}_1\"\u003e and \u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{C}_2\"\u003e share the same latent factors, but \u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{D}_1\"\u003e and \u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{D}_2\"\u003e have uncorrelated latent factors.\n\nNote that \u003cimg src=\"https://render.githubusercontent.com/render/math?math=\\boldsymbol{Y}_k\"\u003e should be row-mean centered.\n\nPlease cite the article [1] for this package, which is available [here](https://www.researchgate.net/publication/329691934_D-CCA_A_Decomposition-based_Canonical_Correlation_Analysis_for_High-Dimensional_Datasets).\n\n[1] Hai Shu, Xiao Wang \u0026 Hongtu Zhu (2020) D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets. Journal of the American Statistical Association, 115(529): 292-306. [DOI: 10.1080/01621459.2018.1543599](https://doi.org/10.1080/01621459.2018.1543599) \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshu-hai%2FD-CCA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshu-hai%2FD-CCA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshu-hai%2FD-CCA/lists"}