SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Koptagel, H.; Jun, S.-H.; Lagergren, J.

doi:10.1101/357442

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bioRxiv · 10.1101/357442

SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Koptagel, H.·Jun, S.-H.·Lagergren, J.

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Abstract

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The methods robustness is investigated using various synthetic datasets and a biological dataset of 18 cells. Author summaryCell lineage tree reconstruction carries a significant potential for studies of development and medicine. The lineage tree reconstruction task is especially challenging for cells taken from healthy tissue due to the scarcity of mutations. In addition, the single-cell whole-genome sequencing technology introduces artifacts such as amplification errors, allelic dropouts, and sequencing errors. We propose Scuphr, a probabilistic framework to reconstruct cell lineage trees. We designed Scuphr for single-cell DNA sequencing data; it accounts for technological artifacts in its graphical model and uses germline heterozygous sites to improve its accuracy. Scuphr is embarrassingly parallel; the speed of the computational analysis is inversely proportional to the number of available computational nodes. We demonstrated that Scuphr is fast, robust, and more accurate than the state-of-the-art method with the synthetic data experiments. Moreover, in the biological data experiment, we showed Scuphr successfully identifies different clones and further obtains more support on closely related cells within clones.

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Koptagel, H., Jun, S.-H., Lagergren, J.. 2018-06-29. SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction. https://doi.org/10.1101/357442

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