{ "_id": "6a5b68f89baadeef8ab4dad5", "Package": "barmixR", "Type": "Package", "Title": "Bayesian Modeling of Barcoded Tumor Mixtures for Quantitative\nTreatment Resistance Analysis", "Version": "0.99.2", "Authors@R": "c(\nperson(given = \"Mohammad\", family = \"Darbalaei\",\nemail = \"mohammad.darbalaei@uni-due.de\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0003-4561-0048\")),\nperson(given = \"Daniel\", family = \"Hoffmann\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0003-2973-7869\")),\nperson(given = \"Barbara M.\", family = \"Grüner\",\nrole = \"ctb\"),\nperson(given = \"Thomas\", family = \"Mühlenberg\",\nrole = \"ctb\"),\nperson(given = \"Julia\", family = \"Zummack\",\nrole = \"ctb\")\n)", "Description": "Implements the Bayesian modeling framework underlying the\nbarmixR (BARcode MIXture analysis) platform for high-throughput\nquantitative analysis of genotype-specific treatment responses\nin pooled cancer cell populations. The package integrates\nbarcode sequencing count data with volumetric measurements such\nas tumor volume (in vivo) or cellular confluency (in vitro)\nusing hierarchical probabilistic models. Barcode counts are\nmodeled with a Dirichlet–multinomial distribution to account\nfor compositional sequencing data, while volumetric\nmeasurements are modeled using log-normal (tumor volume) or\nbeta (confluency) likelihoods. Posterior inference is performed\nusing Hamiltonian Monte Carlo through 'rstan'. The resulting\nposterior distributions enable estimation of clone-specific\nquantitative treatment resistance (QTR) together with\nuncertainty propagation from both sequencing and volumetric\ndata. Additional functions provide posterior predictive checks,\nestimation of resistance ratios, treatment ranking, and\nvisualization of resistance landscapes using violin plots and\nbubble heatmaps. The methods are designed for multiplexed\nlineage-tracing experiments in cancer research and were\ndeveloped to analyze treatment resistance in gastrointestinal\nstromal tumors (GIST), but are broadly applicable to\nbarcoding-based studies of treatment response and clonal\ndynamics across diverse cancer types.", "License": "GPL-3 + file LICENSE", "URL": "https://github.com/MohammadDarbalaei/barmixR", "BugReports": "https://github.com/MohammadDarbalaei/barmixR/issues", "Encoding": "UTF-8", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE)", "biocViews": "Software, Bayesian, Sequencing, Visualization", "Config/roxygen2/version": "8.0.0", "Biarch": "true", "SystemRequirements": "GNU make, C++17, StanHeaders", "Config/pak/sysreqs": "make", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-06-27 20:08:12 UTC", "RemoteUrl": "https://github.com/bioc/barmixR", "RemoteRef": "HEAD", "RemoteSha": "1509ed7e005f7de0cd6fdb50f7e290a4f903ea2d", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-18 11:40:30 UTC", "User": "root" }, "Author": "Mohammad Darbalaei [aut, cre] (ORCID:\n),\nDaniel Hoffmann [aut] (ORCID: ),\nBarbara M. 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