{ "_id": "6a1eb79bb25058d4daade800", "Package": "DrDimont", "Type": "Package", "Title": "Drug Response Prediction from Differential Multi-Omics Networks", "Version": "0.1.6", "Authors@R": "c(\nperson(\"Katharina\", \"Baum\", email = \"katharina.baum@fu-berlin.de\", role = \"cre\",\ncomment = c(ORCID = \"0000-0001-7256-0566\")),\nperson(\"Pauline\", \"Hiort\", email = \"pauline.hiort@hpi.de\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-3530-7358\")),\nperson(\"Julian\", \"Hugo\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-3355-1071\")),\nperson(\"Spoorthi\", \"Kashyap\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-5474-8183\")),\nperson(\"Nataniel\", \"Müller\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-0275-3992\")),\nperson(\"Justus\", \"Zeinert\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-3918-0507\"))\n)", "Description": "While it has been well established that drugs affect and\nhelp patients differently, personalized drug response\npredictions remain challenging. Solutions based on single omics\nmeasurements have been proposed, and networks provide means to\nincorporate molecular interactions into reasoning. However, how\nto integrate the wealth of information contained in multiple\nomics layers still poses a complex problem. We present a novel\nnetwork analysis pipeline, DrDimont, Drug response prediction\nfrom Differential analysis of multi-omics networks. It allows\nfor comparative conclusions between two conditions and\ntranslates them into differential drug response predictions.\nDrDimont focuses on molecular interactions. It establishes\ncondition-specific networks from correlation within an omics\nlayer that are then reduced and combined into heterogeneous,\nmulti-omics molecular networks. A novel semi-local, path-based\nintegration step ensures integrative conclusions. Differential\npredictions are derived from comparing the condition-specific\nintegrated networks. DrDimont's predictions are explainable,\ni.e., molecular differences that are the source of high\ndifferential drug scores can be retrieved. Our proposed\npipeline leverages multi-omics data for differential\npredictions, e.g. on drug response, and includes prior\ninformation on interactions. The case study presented in the\nvignette uses data published by Krug (2020)\n. The package license applies\nonly to the software and explicitly not to the included data.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 10:50:54 UTC", "User": "root" }, "Author": "Katharina Baum [cre] (ORCID:\n), Pauline Hiort [aut]\n(ORCID: ), Julian Hugo\n[aut] (ORCID: ),\nSpoorthi Kashyap [aut] (ORCID:\n), Nataniel Müller [aut]\n(ORCID: ), Justus\nZeinert [aut] (ORCID: )", "Maintainer": "Katharina Baum ", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibuv1-dev libxml2-dev python3 libx11-dev", "Repository": "https://kathbaum.r-universe.dev", "Date/Publication": "2025-11-08 13:00:02 UTC", "RemoteUrl": "https://github.com/cran/DrDimont", "RemoteRef": "HEAD", "RemoteSha": "13b9430ca8719965376296f42574df87004366e0", "MD5sum": "968b628b972fcd7a224f6f5e0e3cec53", "_user": "kathbaum", "_type": "src", "_file": "DrDimont_0.1.6.tar.gz", "_fileid": "8d63c55476361fe4fcf93c1310fa1c1510bab3d9255c3f3b187cf63af9745c16", "_filesize": 1405416, "_sha256": "8d63c55476361fe4fcf93c1310fa1c1510bab3d9255c3f3b187cf63af9745c16", "_created": "2026-06-02T10:50:54.000Z", "_published": "2026-06-02T10:59:39.103Z", "_distro": "noble", "_jobs": [ { "job": 79054369400, "time": 176, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7355736368" }, { "job": 79054369419, "time": 165, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7355731903" }, { "job": 79054369408, "time": 111, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7355834991" }, { "job": 79054369427, "time": 95, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7355840534" }, { "job": 79053625237, "time": 275, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7355675506" }, { "job": 79054369401, "time": 173, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7355735205" }, { "job": 79054369462, "time": 395, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7355808896" }, { "job": 79054369476, "time": 346, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7355792992" }, { "job": 79054369447, "time": 484, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7355838286" } ], "_buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/DrDimont", "_commit": { "id": "13b9430ca8719965376296f42574df87004366e0", "author": "Katharina Baum ", "committer": "cran-robot ", "message": "version 0.1.6\n", "time": 1762606802 }, "_maintainer": { "name": "Katharina Baum", "email": "katharina.baum@fu-berlin.de", "login": "kathbaum", "description": "", "uuid": 84584704, "orcid": "0000-0001-7256-0566" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "igraph", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "WGCNA", "role": "Imports" }, { "package": "Rfast", "role": "Imports" }, { "package": "readr", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "reticulate", "role": "Imports" }, { "package": "GO.db", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-45", "n": 1 } ], "_tags": [ { "name": "0.1.6", "date": "2025-11-08" } ], "_stars": 0, "_contributors": [ { "user": "kathbaum", "count": 3, "uuid": 84584704 } ], "_userbio": { "uuid": 84584704, "type": "user", "name": "kathbaum" }, "_downloads": { "count": 120, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/DrDimont" }, "_searchresults": 2, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/DrDimont.html", "manual.pdf" ], "_realowner": "kathbaum", "_cranurl": false, "_releases": [ { "version": "0.1.3", "date": "2022-05-17" }, { "version": "0.1.4", "date": "2022-09-23" }, { "version": "0.1.6", "date": "2025-11-08" } ], "_exports": [ "%>%", "check_input", "compute_correlation_matrices", "compute_drug_response_scores", "determine_drug_targets", "drdimont_settings", "generate_combined_graphs", "generate_differential_score_graph", "generate_individual_graphs", "generate_interaction_score_graphs", "install_python_dependencies", "make_connection", "make_drug_target", "make_layer", "return_errors", "run_pipeline" ], "_datasets": [ { "name": "combined_graphs_example", "title": "Combined graphs", "object": "combined_graphs_example", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "correlation_matrices_example", "title": "Correlation matrices", "object": "correlation_matrices_example", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "differential_graph_example", "title": "Differential graph", "object": "differential_graph_example", "class": [ "igraph" ], "fields": [], "table": false, "tojson": false }, { "name": "drug_gene_interactions", "title": "Drug-gene interactions", "object": "drug_gene_interactions", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "gene_name", "drug_name", "drug_chembl_id" ], "rows": 22972, "table": true, "tojson": true }, { "name": "drug_response_scores_example", "title": "Drug response score", "object": "drug_response_scores_example", "class": [ "data.frame" ], "fields": [ "drug_name", "drug_response_score" ], "rows": 261, "table": true, "tojson": true }, { "name": "drug_target_edges_example", "title": "Drug target nodes in combined network", "object": "drug_target_edges_example", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "individual_graphs_example", "title": "Individual graphs", "object": "individual_graphs_example", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "interaction_score_graphs_example", "title": "Interaction score graphs", "object": "interaction_score_graphs_example", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "layers_example", "title": "Formatted layers object", "object": "layers_example", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "metabolite_data", "title": "Metabolomics data", "object": "metabolite_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "metabolite_protein_interactions", "title": "Metabolite protein interaction data", "object": "metabolite_protein_interactions", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "pubchem_id", "gene_name", "combined_score" ], "rows": 55499, "table": true, "tojson": true }, { "name": "mrna_data", "title": "mRNA expression data", "object": "mrna_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "phosphosite_data", "title": "Phosphosite data", "object": "phosphosite_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "protein_data", "title": "Protein data", "object": "protein_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "check_input", "title": "Check pipeline input data for required format", "topics": [ "check_input" ] }, { "page": "combined_graphs_example", "title": "Combined graphs", "topics": [ "combined_graphs_example" ] }, { "page": "compute_correlation_matrices", "title": "Computes correlation matrices for specified network layers", "topics": [ "compute_correlation_matrices" ] }, { "page": "compute_drug_response_scores", "title": "Calculate drug response score", "topics": [ "compute_drug_response_scores" ] }, { "page": "correlation_matrices_example", "title": "Correlation matrices", "topics": [ "correlation_matrices_example" ] }, { "page": "determine_drug_targets", "title": "Determine drug target nodes in network", "topics": [ "determine_drug_targets" ] }, { "page": "differential_graph_example", "title": "Differential graph", "topics": [ "differential_graph_example" ] }, { "page": "drdimont_settings", "title": "Create global settings variable for DrDimont pipeline", "topics": [ "drdimont_settings" ] }, { "page": "drug_gene_interactions", "title": "Drug-gene interactions", "topics": [ "drug_gene_interactions" ] }, { "page": "drug_response_scores_example", "title": "Drug response score", "topics": [ "drug_response_scores_example" ] }, { "page": "drug_target_edges_example", "title": "Drug target nodes in combined network", "topics": [ "drug_target_edges_example" ] }, { "page": "generate_combined_graphs", "title": "Combines individual layers to a single graph", "topics": [ "generate_combined_graphs" ] }, { "page": "generate_differential_score_graph", "title": "Compute difference of interaction score of two groups", "topics": [ "generate_differential_score_graph" ] }, { "page": "generate_individual_graphs", "title": "Builds graphs from specified network layers", "topics": [ "generate_individual_graphs" ] }, { "page": "generate_interaction_score_graphs", "title": "Computes interaction score for combined graphs", "topics": [ "generate_interaction_score_graphs" ] }, { "page": "individual_graphs_example", "title": "Individual graphs", "topics": [ "individual_graphs_example" ] }, { "page": "install_python_dependencies", "title": "Installs python dependencies needed for interaction score computation", "topics": [ "install_python_dependencies" ] }, { "page": "interaction_score_graphs_example", "title": "Interaction score graphs", "topics": [ "interaction_score_graphs_example" ] }, { "page": "layers_example", "title": "Formatted layers object", "topics": [ "layers_example" ] }, { "page": "make_connection", "title": "Specify connection between two individual layers", "topics": [ "make_connection" ] }, { "page": "make_drug_target", "title": "Reformat drug-target-interaction data", "topics": [ "make_drug_target" ] }, { "page": "make_layer", "title": "Creates individual molecular layers from raw data and unique identifiers", "topics": [ "make_layer" ] }, { "page": "metabolite_data", "title": "Metabolomics data", "topics": [ "metabolite_data" ] }, { "page": "metabolite_protein_interactions", "title": "Metabolite protein interaction data", "topics": [ "metabolite_protein_interactions" ] }, { "page": "mrna_data", "title": "mRNA expression data", "topics": [ "mrna_data" ] }, { "page": "phosphosite_data", "title": "Phosphosite data", "topics": [ "phosphosite_data" ] }, { "page": "protein_data", "title": "Protein data", "topics": [ "protein_data" ] }, { "page": "return_errors", "title": "Return detected errors in the input data", "topics": [ "return_errors" ] }, { "page": "run_pipeline", "title": "Execute all DrDimont pipeline steps sequentially", "topics": [ "run_pipeline" ] } ], "_rundeps": [ "backports", "base64enc", "bit", "bit64", "bslib", "cachem", "checkmate", "cli", "clipr", "cluster", "codetools", "colorspace", "cpp11", "crayon", "data.table", "digest", "doParallel", "dplyr", "dynamicTreeCut", "evaluate", "farver", "fastcluster", "fastmap", "fontawesome", "foreach", "foreign", "Formula", "fs", "generics", "ggplot2", "glue", "gridExtra", "gtable", "here", "highr", "Hmisc", "hms", "htmlTable", "htmltools", "htmlwidgets", "igraph", "impute", "isoband", "iterators", "jquerylib", "jsonlite", "knitr", "labeling", "lattice", "lifecycle", "magrittr", "Matrix", "matrixStats", "memoise", "mime", "nnet", "pillar", "pkgconfig", "png", "preprocessCore", "prettyunits", "progress", "purrr", "R6", "rappdirs", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppParallel", "RcppTOML", "readr", "reticulate", "Rfast", "rlang", "rmarkdown", "rpart", "rprojroot", "rstudioapi", "S7", "sass", "scales", "stringi", "stringr", "survival", "tibble", "tidyr", "tidyselect", "tinytex", "tzdb", "utf8", "vctrs", "viridisLite", "vroom", "WGCNA", "withr", "xfun", "yaml", "zigg" ], "_vignettes": [ { "source": "DrDimont_Vignette.Rmd", "filename": "DrDimont_Vignette.html", "title": "DrDimont: Drug Response Prediction from Differential Multi-Omics Networks", "author": "Katharina Baum, Pauline Hiort, Julian Hugo, Spoorthi Kashyap, Nataniel Mueller, and Justus Zeinert", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Installation", "Installation of Python and its dependencies", "Example Data Set Description", "Load the data", "Transform the data to the required input format", "Create the individual layers data structure from the molecular data", "Create inter-layer connections data structure", "Create drug-target interaction data structure", "Check input data structures", "Run the complete pipeline", "Run the individual pipeline steps", "Step 1: Compute correlation matrices", "Step 2: Generate individual graphs", "Step 3: Combine graphs", "Step 4: Identify drug targets and their edges", "Step 5: Calculate integrated interaction score", "Step 6: Generate differential graph", "Step 7: Calculate differential drug response score", "References" ], "created": "2022-05-17 09:20:02", "modified": "2025-11-08 13:00:02", "commits": 3 } ], "_score": 2, "_indexed": true, "_nocasepkg": "drdimont", "_universes": [ "kathbaum" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.6", "date": "2026-06-02T10:53:44.000Z", "distro": "noble", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "2aa8492918c57294ffaf3049d212f55111bcc303e4b505f39b3af4eba5004250", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.6.0", "os": "linux", "version": "0.1.6", "date": "2026-06-02T10:53:38.000Z", "distro": "noble", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "e4caf438e5b4efdd65699c183a3240f4133800a71a7a983f546c48e9ca65be25", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.5.3", "os": "mac", "version": "0.1.6", "date": "2026-06-02T10:58:47.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "7a42d0185af9e83989da8cf848a32caeeda7c9c1b0e08f805a1b0f18dd1ebad2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.6.0", "os": "mac", "version": "0.1.6", "date": "2026-06-02T10:59:07.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "fe43db03571f380c275a6f345e3e5d7886e452d718de301086bbafbc6a115664", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.6.0", "os": "wasm", "version": "0.1.6", "date": "2026-06-02T10:54:06.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "f28c59ddac23007903e67fcb026d34223145f7aa50e8be9390f06902e87f7ab7", "status": "success", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.7.0", "os": "win", "version": "0.1.6", "date": "2026-06-02T10:57:13.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "5ba1337771ea1350cedea189c520bcecd0490c951ffd60b902276b104b2ad53b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.5.3", "os": "win", "version": "0.1.6", "date": "2026-06-02T10:56:34.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "ce32b93db0bb3f65a8808edc57500ed96d9c05f96f56223d430ac19603ef78bb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" }, { "r": "4.6.0", "os": "win", "version": "0.1.6", "date": "2026-06-02T10:58:47.000Z", "commit": "13b9430ca8719965376296f42574df87004366e0", "fileid": "d184b51a7795e5bdbb8fae6b8310d34f2769d5e33add4faa14d21a1dfa442745", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/kathbaum/actions/runs/26814751658" } ] }