Multiplexed assays such as DNA-barcoded cell line mixtures generate compositional sequencing data that capture only relative clone abundances. This inherent constraint complicates the inference of absolute, clone-specific treatment effects, as changes in relative abundance do not necessarily reflect true growth behavior.
Here, we develop a hierarchical Bayesian modeling framework to infer clone-specific treatment responses by jointly integrating compositional sequencing data with independent measurements of total population size, such as confluency in vitro or tumor volume in vivo. Barcode count data are modeled using a Dirichlet–multinomial likelihood to capture sampling variability and biological overdispersion. Tumor volume and confluency measurements are modeled using log-normal and beta likelihoods, respectively, enabling inference of absolute growth. By combining these data sources, the framework reconstructs clone-specific changes in absolute abundance under treatment relative to control. A hierarchical structure captures variability across replicates and treatment conditions, enabling partial pooling and stabilizing estimates in settings with limited observations. Inference is performed in a fully probabilistic manner, allowing uncertainty from both sequencing and population-level measurements to be propagated to the final estimates. This yields a quantitative measure of treatment response for each clone with associated uncertainty, resolving ambiguities inherent to compositional data.
We validated the method by comparison of experimental data from treatment responses in cancer cell line mixtures and individual cell lines. This approach provides a general framework for multiplexed drug screening assays and reduces the number of required animal experiments through pooled experimental designs.

Co-authors: Julia Zummack, Thomas Mühlenberg, Philip Dujardin, Susanne Grunewald, Patricia Munteanu, Marina Martinez Cruz, Madeleine Dorsch, Alexander Schramm, Sebastian Bauer, Barbara M. Grüner, Daniel Hoffmann