Package: barmixR
Type: Package
Title: Bayesian Modeling of Barcoded Tumor Mixtures for Quantitative
        Treatment Resistance Analysis
Version: 0.99.0
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
URL: https://github.com/MohammadDarbalaei/barmixR
BugReports: https://github.com/MohammadDarbalaei/barmixR/issues
Encoding: UTF-8
Depends: R (>= 4.3.0)
Imports: rstan, ggplot2, dplyr, patchwork, forcats, BiocParallel, stats
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
biocViews: Software, Bayesian, Sequencing, Visualization
NeedsCompilation: no
Packaged: 2026-05-04 17:23:07 UTC; pkgbuild
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>
