https://mdarbalaei.r-universe.devRecent package updates in mdarbalaeiR-universehttps://github.com/mdarbalaei.pnghttps://mdarbalaei.r-universe.devSat, 12 Sep 2026 23:19:43 GMTmohammad.darbalaei@uni-due.de (Mohammad Darbalaei)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.https://github.com/r-universe/bioc/actions/runs/38389193861Sat, 12 Sep 2026 23:19:43 GMTbarmixR0.99.3successhttps://bioc.r-universe.devhttps://github.com/bioc/barmixRbarmixR-workflow.RmdbarmixR-workflow.htmlbarmixR: Bayesian Modeling of Barcoded Tumor Mixtures for Quantitative Treatment Resistance Analysis2026-03-22 20:58:022026-06-27 19:51:10