{"id":20074703,"url":"https://github.com/greenelab/gbm_immune_validation","last_synced_at":"2025-07-07T12:08:08.470Z","repository":{"id":79359394,"uuid":"72469680","full_name":"greenelab/gbm_immune_validation","owner":"greenelab","description":"Validating glioblastoma immune cell immunohistochemsitry using computational deconvolution of TCGA tumors","archived":false,"fork":false,"pushed_at":"2019-07-10T12:07:17.000Z","size":2588,"stargazers_count":14,"open_issues_count":0,"forks_count":6,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-05-23T00:26:42.379Z","etag":null,"topics":["analysis","cancer","data-science","gene-expression","glioblastoma","machine-learning","survival-analysis","tool"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/greenelab.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}},"created_at":"2016-10-31T19:14:12.000Z","updated_at":"2022-12-04T15:11:25.000Z","dependencies_parsed_at":"2023-03-09T04:15:31.862Z","dependency_job_id":null,"html_url":"https://github.com/greenelab/gbm_immune_validation","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/greenelab/gbm_immune_validation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Fgbm_immune_validation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Fgbm_immune_validation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Fgbm_immune_validation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Fgbm_immune_validation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/greenelab","download_url":"https://codeload.github.com/greenelab/gbm_immune_validation/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Fgbm_immune_validation/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264075631,"owners_count":23553509,"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":["analysis","cancer","data-science","gene-expression","glioblastoma","machine-learning","survival-analysis","tool"],"created_at":"2024-11-13T14:53:41.705Z","updated_at":"2025-07-07T12:08:08.444Z","avatar_url":"https://github.com/greenelab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Investigating immune cell profiles across Glioblastoma subtypes\n\n**Gregory Way and Casey Greene**\n**University of Pennsylvania**\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.195238.svg)](https://doi.org/10.5281/zenodo.195238)\n\n[![Build Status](http://165.123.67.152/api/badges/greenelab/gbm_immune_validation/status.svg)](http://165.123.67.152/greenelab/gbm_immune_validation)\n\n\n## Summary\n\nThe amount and type of immune cell infiltration into tumors is an important\ndeterminant of disease progression and survival. Cancer subtypes can often be\ndistinguished by the rate of immune cell infiltration since this tends to be one\nof the more dominant observable signatures in gene expression data. Current\nmethods for directly observing immune cell profiles in a given population\ninclude laboriously quantifying immune cell proportions by flow cytometry or\nother technically challenging cell labeling techniques. Therefore, deconvolution\nmethods are being developed to automatically extract immune cell proportions\nfrom full tumor gene expression data.\n\nWe used ssGSEA ([Barbie et al. 2009](http:/doi.org/10.1038/nature08460 \"ssGSEA\"))\nto deconvolute immune cell signatures from glioblastoma multiforme (GBM) tumors\nfrom The Cancer Genome Atlas. Briefly, ssGSEA is a simple rank based test that\nevaluates the empirical cumulative distribution function of input gene sets\ncompared to the eCDF of the remaining genes.\n\nWe used LM22.txt as defined by\n[Newman et al. 2015](http://doi.org/10.1038/nmeth.3337 \"CIBERSORT\") as input\ngenelists to ssGSEA.\n\n## Reproducibility\n\nOur end to end analysis from downloading data to generating publication ready\nfigures is provided in this github repository. We implement an automatic \n[reproducible workflow using continuous analysis](http://doi.org/10.1101/056473 \"Beaulieu-Jones and Greene 2016\")\nto ensure a stable compute environment and consistent reproducibility.\n\nWe use the ssGSEA implementation available on\n[bioconductor](https://bioconductor.org/packages/release/bioc/html/GSVA.html)\n(Guinney and Castelo 2016).\n\n```bash\n# To reproduce the pipeline independently simply run:\nbash run_pipeline.sh\n```\n\nFor exact instructions on how to reproduce our analysis see `run_pipeline.sh`.\n\n## Results\n\nOur _in silico_ deconvolution of CD4+ cells, CD8+ cells, and Macrophages in TCGA\ndata matches very closely to immunohistochemistry estimates of the same cell types\nin a separate dataset. The proportions of immune cell infiltrate across subtypes\ncorresponds strongly.\n\n![immune deconvolution and IHC](figures/boxplot_validation_TCGA_summary.png?raw=true)\n\nWe also observed that high macrophage infiltration was associated with worse outcomes\nin the TCGA dataset. This relationship was strengthed after adjusting for several\ncovariates including age, gender, and gene expression based subtype.\n\n![TCGA macrophage survival](figures/TCGA_kaplanmeier_Macrophages.png?raw=true)  \n\n\n## Contact\n\nFor all code related questions, bug reporting, or feature requests please file a\n[GitHub issue](https://github.com/greenelab/GBM_immune_profiles/issues \"file an issue\")\n\n## Dependencies\n\nAll analyses were performed in R version 3.2.3 and packages were versioned with the\ncheckpoint package (version 0.3.18) set to a snapshot date of \"2016-08-16\". The checkpoint\npackage will automatically download all the specified packages at the versions they\nexisted in on that specific date. See [install.R](install.R) for more details.\nThe versions for each package are specified in [sessionInfo.txt](sessionInfo.txt).\n\nWe also provide a [Docker image](https://hub.docker.com/r/gregway/gbm_immune_validation/)\nto recreate the compute environment. See the [Dockerfile](Dockerfile) for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Fgbm_immune_validation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgreenelab%2Fgbm_immune_validation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Fgbm_immune_validation/lists"}