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Multi-norm constrained optimization methods for calling copy number variants in single cell sequencing data

机译：用于在单细胞测序数据中调用拷贝数变异的多范数约束优化方法

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The revolutionary invention of single-cell sequencing technology carves out a new way to delineate intra tumor heterogeneity and the evolution of single cells at the molecular level. Since single-cell sequencing requires a special genome amplification step to accumulate enough samples, a large number of bias were introduced, making the calling of copy number variants rather challenging. Accurately modeling this process and effectively detecting copy number variations (CNVs) are the major roadblock for single-cell sequencing data analysis. Recent advances manifested that the underlying copy numbers are corrupted by noise, which could be approximated by negative binomial distribution. In this paper, we formulated a general mathematical model for copy number reconstruction from read depth signal, and presented its two specific variants, namely Poisson-CNV and NB-CNV to catering for various reads distribution. Efficient numerical solution based on the classical alternating direction minimization method was designed to solve the proposed models. Extensive experiments on both synthetic datasets and empirical single-cell sequencing datasets were conducted to compare the performance of the two models. The results show that the proposed model of NB-CNV achieved superior performance in calling the CNV for single-cell sequencing data.
机译：单细胞测序技术的革命性发明开辟了一种在分子水平上描述肿瘤内异质性和单细胞进化的新方法。由于单细胞测序需要特殊的基因组扩增步骤来积累足够的样品，因此引入了大量的偏倚，使得拷贝数变异的调用颇具挑战性。准确地对此过程进行建模并有效地检测拷贝数变异（CNV）是单细胞测序数据分析的主要障碍。最近的进展表明，潜在的拷贝数被噪声破坏，可以通过负二项分布来近似。在本文中，我们建立了一个通用的数学模型，用于从读取深度信号中重建拷贝数，并提出了其两个特定的变体Poisson-CNV和NB-CNV以适应各种读取分布。设计了基于经典交替方向最小化方法的有效数值解来求解所提出的模型。在合成数据集和经验单细胞测序数据集上进行了广泛的实验，以比较这两种模型的性能。结果表明，所提出的NB-CNV模型在调用CNV进行单细胞测序数据时取得了优异的性能。

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