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 the reliable statistical methodology, we illustrate how multidimensional phenotypic profiling enables one to deconvolute phenotypically distinct clonal subpopulations within a cancer cell line. The mixture control dataset and our analysis results provide a foundation for benchmarking and improving algorithms for clone-tracing experiments.

中文翻译：

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改进了高通量克隆表型组学中差异表达 DNA 条形码的检测。

细胞 DNA 条形码已成为研究细胞群异质性和识别对细胞刺激具有差异反应的克隆的流行方法。然而，缺乏可靠的方法对差异响应克隆进行统计推断。在这里，我们使用 DNA 条形码细胞池的混合物来生成真实的基准读取计数数据集，用于对克隆追踪实验的一系列结果进行建模。通过考虑 DNA 条形码读取计数数据固有的统计特性，我们实施了一种改进的算法，与当前的 RNA-seq 数据分析算法相比，该算法可显着降低假阳性率，特别是在实验中检测差异响应克隆时强大的选择压力。基于可靠的统计方法，我们说明了多维表型分析如何使人们能够对癌细胞系内表型不同的克隆亚群进行解卷积。混合物控制数据集和我们的分析结果为克隆追踪实验的基准测试和改进算法提供了基础。