barbieQ: an R software package for analysing barcode count data from clonal tracking experiments

Area of Science:

• * Computational Biology
• * Genomics
• * Bioinformatics

Background:

• * Single-cell lineage tracing utilizes DNA barcodes to track cell populations and their progeny.
• * Current analysis of barcode count data lacks a standardized, robust workflow, often relying on bespoke methods.
• * A generalized approach is needed for preprocessing and statistical analysis of barcode count data.

Purpose of the Study:

• * To introduce barbieQ, a Bioconductor R package for analyzing barcode count data.
• * To provide data-driven quality control, filtering, and visualization tools.
• * To implement robust statistical tests for differential barcode analysis.

Main Methods:

• * Development of the barbieQ R package available on Bioconductor.
• * Implementation of two statistical tests: differential barcode proportion and differential barcode occurrence.
• * Utilizing regression models to handle complex experimental designs and account for sample variability.

Main Results:

• * barbieQ offers data-driven quality control and filtering capabilities.
• * The package includes extensive visualizations for barcode count data.
• * Validated statistical tests demonstrate appropriate statistical properties (size and power) and outperform existing methods.

Conclusions:

• * barbieQ provides a generalized and robust workflow for analyzing single-cell lineage tracing barcode data.
• * The package enhances the statistical rigor and analytical power for clonal tracking studies.
• * barbieQ facilitates more reliable comparisons of clonal composition across different experimental groups.