{"id":24529042,"url":"https://github.com/saezlab/footprints","last_synced_at":"2025-07-13T20:34:23.520Z","repository":{"id":30371405,"uuid":"33923991","full_name":"saezlab/footprints","owner":"saezlab","description":"Analysis code for \"Perturbation-response genes reveal signaling footprints in cancer gene expression\"","archived":false,"fork":false,"pushed_at":"2018-05-11T12:03:10.000Z","size":23743,"stargazers_count":20,"open_issues_count":5,"forks_count":8,"subscribers_count":20,"default_branch":"master","last_synced_at":"2025-04-14T17:11:24.835Z","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":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saezlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-04-14T09:53:15.000Z","updated_at":"2024-01-29T13:16:59.000Z","dependencies_parsed_at":"2022-09-08T08:50:20.879Z","dependency_job_id":null,"html_url":"https://github.com/saezlab/footprints","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/saezlab/footprints","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Ffootprints","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Ffootprints/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Ffootprints/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Ffootprints/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/footprints/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Ffootprints/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265200004,"owners_count":23726750,"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":"2025-01-22T07:34:32.247Z","updated_at":"2025-07-13T20:34:23.500Z","avatar_url":"https://github.com/saezlab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"Analysis code for: \"Perturbation-response genes reveal signaling footprints in cancer gene expression\"\n======================================================================================================\n\nNumerous pathway methods have been developed to quantify the signaling state of\na cell from gene expression data, usually from the abundance of transcripts of\npathway members, and are hence unable to take into account post-translational\ncontrol of signal transduction. Gene expression signatures of pathway\nperturbations can capture this, but they are closely tied to the experimental\nconditions that they were derived from. We overcome both limitations by\nleveraging a large compendium of publicly available perturbation experiments to\ndefine consensus signatures for pathway activity. We find that although\nindividual expression signatures are heterogeneous, there is a common core of\nresponsive genes that describe pathway activation in a wide range of\nconditions. These signaling footprints better recover pathway activity than\nexisting methods and provide more meaningful associations with (i) known driver\nmutations in primary tumors, (ii) drug response in cell lines, and (iii)\nsurvival in cancer patients, making them more suitable to assess the activity\nstatus of signaling pathways.\n\nThe corresponding article for this project is available on\n[bioRxiv](http://biorxiv.org/content/early/2016/07/25/065672)\n([pdf](http://biorxiv.org/content/early/2016/07/25/065672.full.pdf)).\n\n```\n@article {Schubert-PRGs,\n\tauthor = {Schubert, Michael and Klinger, Bertram and Kl{\\\"u}nemann, Martina and \n              Garnett, Mathew J and Bl{\\\"u}thgen, Nils and Saez-Rodriguez, Julio},\n\ttitle = {Perturbation-response genes reveal signaling footprints in cancer gene expression},\n\tyear = {2016},\n\tdoi = {10.1101/065672},\n\tpublisher = {Cold Spring Harbor Labs Journals},\n\tURL = {http://biorxiv.org/content/early/2016/07/25/065672},\n\teprint = {http://biorxiv.org/content/early/2016/07/25/065672.full.pdf},\n\tjournal = {bioRxiv}\n}\n```\n\nPerturbation experiments\n------------------------\n\nThe 11 pathways comprised of 208 submissions to\n[ArrayExpress](https://www.ebi.ac.uk/arrayexpress/) with a total of 580\nexperiments are available in the [index](index) directory in\n[YAML](https://en.wikipedia.org/wiki/YAML) format. We consider the following\npathways:\n\n * EGFR\n * Hypoxia\n * JAK-STAT\n * MAPK\n * NFkB\n * p53/DDR (DNA damage response)\n * PI3K\n * TGFb\n * Trail\n * VEGF (and PDGF)\n\nSearch terms are supplied in files with name `query.txt`. Files and experiments\nwe excluded (because of QC failure or we were not sure if the perturbation\ncorresponds to the phenotype we want to observe) are indicated using the\n`.excluded` suffix or a commented entry in the file. Reason for exclusion is\nmentioned in the files.\n\nZ-scores from gene expression data\n----------------------------------\n\nScripts to download and transform gene expression data, and to generate\nz-scores for the perturbation experiments are in the [data](data) directory.\n\nWe normalized and QC'd each series as whole (`normalize_data.r`), then\nassembled the relevant expression data (`expr.r`) and computed z-scores for\neach perturbation experiment (`zscores.r`).\n\nBuilding the model\n------------------\n\nThe models we built are available in the [model](model) directory. The one we\nused in the publication is called `speed_matrix`.\n\nFor this, we fit a linear model on the z-scores with a binary matrix indicating\npathway perturbations (incl. perturbations of multiple pathways) as the\nindependent variable. We select the 100 most significant genes and use their\nz-scores as coefficients in the model.\n\nComputing pathway scores\n------------------------\n\n#### Different pathway methods considered\n\nWe computed [pathway scores](scores) for the PRGs, and the [corresponding\npathways](config/pathway_mapping.yaml) for Gene Ontology and Reactome genesets\n(using GSVA), Signaling Pathway Impact Analysis (SPIA;\n[article](http://bioinformatics.oxfordjournals.org/content/25/1/75.short)\nand [R package](http://bioconductor.org/packages/release/bioc/html/SPIA.html)),\nPathifier ([article](http://www.pnas.org/content/110/16/6388.short),\n[R package](http://bioconductor.org/packages/release/bioc/html/pathifier.html)),\nand PARADIGM\n([article](http://bioinformatics.oxfordjournals.org/content/26/12/i237.short),\n[tool](https://github.com/sbenz/Paradigm) using the [TCGA signaling\nnetwork](https://tcga-data.nci.nih.gov/docs/publications/coadread_2012/)).\n\n#### On the perturbation experiments\n\nFor the [pathway scores derived from perturbations](scores/speed), we used the\nfold changes (PRGs, Gene Ontology, Reactome) or the basal samples and perturbed\nsamples as control and perturbed, respectively (SPIA, Pathifier).\n\n#### On primary tumors of TCGA (The Cancer Genome Atlas)\n\nWe took TCGA data from [firehose.org](http://firebrowse.org/) and computed all\npathway scores for primary tumors (`01A` in barcode) where a tissue-matched\nnormal (`11A`) was available (required for SPIA and Pathifier).\n\n#### On cell lines of the GDSC (Genomics of Drug Sensitivity in Cancer)\n\nWe took the GDSC data from\n[cancerrxgene.org/gdsc1000](http://www.cancerrxgene.org/gdsc1000)\n([article](http://www.sciencedirect.com/science/article/pii/S0092867416307462)),\ncomputing pathway scores for each cell line (for SPIA and Pathifier compared to\nall other cell lines of the same TCGA label).\n\nAnalyses\n--------\n\n#### Recall of perturbations (Fig. 2)\n\nAnalyses available in [analyses/perturbation_recall](analyses/perturbation_recall).\n\n#### Functional impact of driver mutations (Fig. 3)\n\nAnalyses available in [analyses/tcga_mutation](analyses/tcga_mutation).\n\n#### Explaining drug sensitivity (Fig. 4)\n\nAnalyses available in [analyses/drug_assocs](analyses/drug_assocs).\n\n#### Effect on patient survival (Fig. 5)\n\nAnalyses available in [analyses/tcga_survival](analyses/tcga_survival).\n\n#### Supplementary Figures\n\nAuto-generated using `knitr`, scripts available in the [report directory](report).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Ffootprints","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Ffootprints","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Ffootprints/lists"}