Package: barmixR
Type: Package
Title: Bayesian Modeling of Barcoded Tumor Mixtures for Quantitative
        Treatment Resistance Analysis
Version: 0.99.2
Authors@R: 
    c(
      person(given = "Mohammad", family = "Darbalaei",
             email = "mohammad.darbalaei@uni-due.de",
             role = c("aut", "cre"),
             comment = c(ORCID = "0009-0003-4561-0048")),
      person(given = "Daniel", family = "Hoffmann",
             role = "aut",
             comment = c(ORCID = "0000-0003-2973-7869")),
      person(given = "Barbara M.", family = "Grüner",
             role = "ctb"),
      person(given = "Thomas", family = "Mühlenberg",
             role = "ctb"),
      person(given = "Julia", family = "Zummack",
             role = "ctb")
    )
Description: Implements the Bayesian modeling framework underlying the
        barmixR (BARcode MIXture analysis) platform for high-throughput
        quantitative analysis of genotype-specific treatment responses
        in pooled cancer cell populations. The package integrates
        barcode sequencing count data with volumetric measurements such
        as tumor volume (in vivo) or cellular confluency (in vitro)
        using hierarchical probabilistic models. Barcode counts are
        modeled with a Dirichlet–multinomial distribution to account
        for compositional sequencing data, while volumetric
        measurements are modeled using log-normal (tumor volume) or
        beta (confluency) likelihoods. Posterior inference is performed
        using Hamiltonian Monte Carlo through 'rstan'. The resulting
        posterior distributions enable estimation of clone-specific
        quantitative treatment resistance (QTR) together with
        uncertainty propagation from both sequencing and volumetric
        data. Additional functions provide posterior predictive checks,
        estimation of resistance ratios, treatment ranking, and
        visualization of resistance landscapes using violin plots and
        bubble heatmaps. The methods are designed for multiplexed
        lineage-tracing experiments in cancer research and were
        developed to analyze treatment resistance in gastrointestinal
        stromal tumors (GIST), but are broadly applicable to
        barcoding-based studies of treatment response and clonal
        dynamics 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
Depends: R (>= 4.3.0)
Imports: utils, BiocParallel, dplyr, forcats, ggplot2, methods,
        patchwork, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>=
        2.18.1), rstantools (>= 2.4.0), stats
Suggests: BiocStyle, knitr, MGLM, rmarkdown, testthat, tidyverse
Config/testthat/edition: 3
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
biocViews: Software, Bayesian, Sequencing, Visualization
Config/roxygen2/version: 8.0.0
Biarch: true
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
        RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
        2.18.0)
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: 2026-07-18 11:40:30 UTC; root
Author: Mohammad Darbalaei [aut, cre] (ORCID:
    <https://orcid.org/0009-0003-4561-0048>),
  Daniel Hoffmann [aut] (ORCID: <https://orcid.org/0000-0003-2973-7869>),
  Barbara M. Grüner [ctb],
  Thomas Mühlenberg [ctb],
  Julia Zummack [ctb]
Maintainer: Mohammad Darbalaei <mohammad.darbalaei@uni-due.de>
Built: R 4.6.1; x86_64-w64-mingw32; 2026-07-18 11:44:34 UTC; windows
Archs: x64
