{"id":23810642,"url":"https://github.com/jaydu1/pii","last_synced_at":"2026-04-28T00:30:19.326Z","repository":{"id":269848849,"uuid":"908646100","full_name":"jaydu1/PII","owner":"jaydu1","description":"Post-integrated inference","archived":false,"fork":false,"pushed_at":"2024-12-26T15:37:52.000Z","size":1391,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-03T23:31:15.908Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jaydu1.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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-26T15:34:48.000Z","updated_at":"2024-12-26T15:37:56.000Z","dependencies_parsed_at":"2024-12-26T16:41:07.892Z","dependency_job_id":null,"html_url":"https://github.com/jaydu1/PII","commit_stats":null,"previous_names":["jaydu1/pii"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FPII","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FPII/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FPII/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FPII/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jaydu1","download_url":"https://codeload.github.com/jaydu1/PII/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240063928,"owners_count":19742227,"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":"2025-01-02T00:17:26.438Z","updated_at":"2026-04-28T00:30:19.252Z","avatar_url":"https://github.com/jaydu1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Assumption-Lean Post-Integrated Inference with Negative Control Outcomes\n\nThis repository contains the reproducibility materials for post-integrated inference (PII) procedure from the paper [Du2024b].\n\n\n# Files\n\n## Simulation\n\n- `ex1-simu-PSI.py`: Simulation.\n- `ex2-simu_conv_rate.py`: Evaluate the convergence rate in simulation.\n- `Plot-simu.ipynb`: Plot the results of simulation.\n\n## Real data\n\n- `data/LUHMES.h5`: Preprocessed LUHMES data can be obtained from [[Du2024a]](https://github.com/jaydu1/CausalMultiOutcomes/blob/main/data/LUHMES/LUHMES.h5) and stored to this folder.\n- `ex3-LUHMES_PSI.py`: Apply PSI to the LUHMES data.\n- `ex4-LUHMES_RUV.R`: Apply RUV and CATE to the LUHMES data.\n- `Plot-LUHMES.ipynb`: Plot the results of real data and save common discoveries for GO analysis.\n- `ex3-GO.R`: GO analysis of results.\n\n## Utility functions\n\n- `PII.py`: Post-integrated inference.\n- `cate`: The implementation of CATE [Wang2017]. The original CRAN package is built under an older version of R, which is not compatible with certain new R packages.\n\n\n# Dependencies\n\nThe dependencies for running PII method are listed in `environment.yml` and can be installed by running \n```bash\nconda env create -f environment.yml\n```\n\nThe Python dependencies for reproducibility are listed below:\n\nPackage | Version\n---|---\nh5py | 3.10.0\njoblib | 1.3.2\nmatplotlib-base | 3.8.3\nmatplotlib-venn | 1.1.1\nnumba | 0.59.0\nnumpy | 1.26.4\npandas | 2.2.1\npython | 3.12.2\nscikit-learn | 1.4.1.post1\nscipy | 1.12.0\nstatsmodels | 0.14.4\ntqdm | 4.66.2\nupsetplot | 0.9.0\n\nThe R dependencies for reproducibility are listed below:\n\nPackage | Version\n---|---\nbioconductor-annotationdbi | 1.64.1\nbioconductor-clusterprofiler | 4.10.0\nbioconductor-matrixgenerics | 1.14.0\nbioconductor-org.hs.eg.db | 3.18.0\nbioconductor-rrvgo | 1.14.0\nbioconductor-sva | 3.50.0\nr-base | 4.3.3\nr-biocmanager | 1.30.22\nr-dbplyr | 2.4.0\nr-dplyr | 1.1.4\nr-dqrng | 0.3.2\nr-dtplyr | 1.3.1\nr-esabcv | 1.2.1.1\nr-essentials | 4.3.1\nr-ggplot2 | 3.5.0\nr-hdf5r | 1.3.11\nr-mass | 7.3_60\nr-matrix | 1.6_5\nr-patchwork | 1.2.0\nr-plyr | 1.8.9\nr-purrr | 1.0.2\nr-ruv | 0.9.7.1\nr-seurat | 5.0.1\nr-stringr | 1.5.1\nr-survival | 3.5_8\nr-tidyr | 1.3.1\nr-tidyverse | 2.0.0\n\n\n\n# Reference\n\n- [Wang2017] Wang, J., Zhao, Q., Hastie, T., \u0026 Owen, A. B. (2017). Confounder adjustment in multiple hypothesis testing. Annals of statistics, 45(5), 1863.\n- [Du2024a] Du, J. H., Zeng, Z., Kennedy, E. H., Wasserman, L., \u0026 Roeder, K. (2024). Causal inference for genomic data with multiple heterogeneous outcomes. arXiv preprint arXiv:2404.09119.\n- [Du2024b] Du, J. H., Roeder, K., \u0026 Wasserman, L. (2024). Assumption-Lean Post-Integrated Inference with Negative Control Outcomes. arXiv preprint arXiv:2410.04996.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaydu1%2Fpii","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjaydu1%2Fpii","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaydu1%2Fpii/lists"}