Due to the imperfect fidelity of DNA replication, somatic cells acquire DNA mutations at each division which record their lineage history. Microsatellites, tandem repeats of DNA nucleotide motifs, mutate more frequently than other genomic regions and by observing microsatellite lengths in single cells and implementing suitable inference procedures, the cell lineage tree of an organism can be reconstructed. Due to recent advances in single cell Next Generation Sequencing (NGS) and the phylogenetic methods used to infer lineage trees, this work investigates which computational approaches best exploit the lineage information found in single cell NGS data. We simulated trees representing cell division with mutating microsatellites, and tested a range of available phylogenetic algorithms to reconstruct cell lineage. We found that distance-based approaches are fast and accurate with fully observed data. However, Maximum Parsimony and the computationally intensive probabilistic methods are more robust to missing data and therefore better suited to reconstructing cell lineage from NGS datasets. We also investigated how robust reconstruction algorithms are to different tree topologies and mutation generation models. Our results show that the flexibility of Maximum Parsimony and the probabilistic approaches mean they can be adapted to allow good reconstruction across a range of biologically relevant scenarios.

中文翻译：

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比较缺失数据的微卫星重建细胞谱系的系统发育方法

由于 DNA 复制的不完美保真度，体细胞在记录其谱系历史的每个分裂中都会获得 DNA 突变。微卫星，即 DNA 核苷酸基序的串联重复，比其他基因组区域更频繁地发生突变，通过观察单个细胞中的微卫星长度并实施合适的推理程序，可以重建生物体的细胞谱系树。由于单细胞下一代测序 (NGS) 和用于推断谱系树的系统发育方法的最新进展，这项工作研究了哪些计算方法最能利用单细胞 NGS 数据中发现的谱系信息。我们用变异的微卫星模拟了代表细胞分裂的树，并测试了一系列可用的系统发育算法来重建细胞谱系。我们发现，基于距离的方法对于完全观察到的数据来说既快速又准确。然而，Maximum Parsimony 和计算密集型概率方法对缺失数据更稳健，因此更适合从 NGS 数据集重建细胞谱系。我们还研究了重建算法对不同树拓扑和突变生成模型的鲁棒性。我们的结果表明，Maximum Parsimony 和概率方法的灵活性意味着它们可以适应在一系列生物学相关场景中进行良好的重建。我们还研究了重建算法对不同树拓扑和突变生成模型的鲁棒性。我们的结果表明，Maximum Parsimony 和概率方法的灵活性意味着它们可以适应在一系列生物学相关场景中进行良好的重建。我们还研究了重建算法对不同树拓扑和突变生成模型的鲁棒性。我们的结果表明，Maximum Parsimony 和概率方法的灵活性意味着它们可以适应在一系列生物学相关场景中进行良好的重建。