Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics.

ABSTRACT: Cellular DNA barcoding has become a popular approach to study heterogeneity of cell populations and to identify clones with differential response to cellular stimuli. However, there is a lack of reliable methods for statistical inference of differentially responding clones. Here, we used mixtures of DNA-barcoded cell pools to generate a realistic benchmark read count dataset for modelling a range of outcomes of clone-tracing experiments. By accounting for the statistical properties intrinsic to the DNA barcode read count data, we implemented an improved algorithm that results in a significantly lower false-positive rate, compared to current RNA-seq data analysis algorithms, especially when detecting differentially responding clones in experiments with strong selection pressure. Building on

SUBMITTER: Akimov Y 

PROVIDER: S-EPMC7080434 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature