ArticleBioinformatics (Oxford, England)2026
barbieQ: an R software package for analysing barcode count data from clonal tracking experiments.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationA 'clone' encompasses a progenitor cell and its progeny cells. Tracking clonal composition as cells differentiate or evolve is useful in many fields. Various single-cell lineage tracing (clonal tracking) technologies use unique DNA barcodes that are passed from progenitor cells to their offspring. The barcode count for each sample indicates cell number in clones. However, analysis of barcode count data is often bespoke and relies on visualisations and heuristics. A generalized workflow for preprocessing and robust statistical analysis of barcode count data across protocols is needed.
resultsWe introduce barbieQ, a Bioconductor R package for analysing barcode count data across groups of samples. It provides data-driven quality control and filtering, extensive visualisations, and two statistical tests: (1) Differential barcode proportion (differences in proportions between sample groups), and (2) Differential barcode occurrence (differences in presence/absence odds between groups). Both tests handle complex experimental designs using regression models and rigorously account for sample-to-sample variability. We validated both tests on semi-simulated, real data and a case study, demonstrating that they hold their size, are sufficiently powered to detect true differences, and outperform existing approaches. AVAILABILITY: barbieQ is available on Bioconductor at https://doi.org/10.18129/B9.bioc.barbieQ.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.