{"id":22308590,"url":"https://github.com/sysbiochalmers/cancerproteinsecretionml","last_synced_at":"2025-03-26T01:28:25.709Z","repository":{"id":39406378,"uuid":"119373715","full_name":"SysBioChalmers/CancerProteinSecretionML","owner":"SysBioChalmers","description":"A collection of scripts to analyze cancer transcriptomics data using various statistical and machine-learning approaches.","archived":false,"fork":false,"pushed_at":"2021-03-05T16:19:56.000Z","size":12717,"stargazers_count":0,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-30T22:51:19.562Z","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SysBioChalmers.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}},"created_at":"2018-01-29T11:26:10.000Z","updated_at":"2021-03-27T13:29:23.000Z","dependencies_parsed_at":"2022-09-08T09:42:44.108Z","dependency_job_id":null,"html_url":"https://github.com/SysBioChalmers/CancerProteinSecretionML","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/SysBioChalmers%2FCancerProteinSecretionML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FCancerProteinSecretionML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FCancerProteinSecretionML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FCancerProteinSecretionML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SysBioChalmers","download_url":"https://codeload.github.com/SysBioChalmers/CancerProteinSecretionML/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245571112,"owners_count":20637280,"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":"2024-12-03T20:14:31.267Z","updated_at":"2025-03-26T01:28:25.688Z","avatar_url":"https://github.com/SysBioChalmers.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CancerProteinSecretionML\nAnalysis of gene expression changes in the protein secretory pathway of different cancer types using machine learning.\n\n## Reproducing the analyses in the manuscript\n\n### Code\nThe python and R code necessary to reproduce the analyses in the manuscript can be found in the [scripts](scripts) directory of this repository. View the README therein for further details on the associated scripts.\n\n### Environments\nThere are two conda environment files that define the packages necessary for running the python and R scripts: `environment_python.yml` and `environment_R.yml`, respectively. Create the environments from the files using the following command:\n```\nconda env create -f environment_python.yml\nconda env create -f environment_R.yml\n```\n\nActivate either environment using `conda activate`:\n```\nconda activate psp-cancer-py\n```\n\n_Note:_ The environments were built on MacOS. If you are using a different OS and experience problems when creating either of the environments, try removing the version specified after each package in the `.yml` file (e.g., change `numpy=1.18.1` to `numpy`).\n\n### Data\nNote that you will first need to retrieve the larger data files from the associated Zenodo repository prior to re-running the analyses. This is described in further detail by the README in the [data](data) directory.\n\n\n## Analysis result files\nThe raw analysis output files can be found in the [results](results) directory.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsysbiochalmers%2Fcancerproteinsecretionml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsysbiochalmers%2Fcancerproteinsecretionml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsysbiochalmers%2Fcancerproteinsecretionml/lists"}