Scuphr: A probabilistic framework for cell lineage tree reconstruction

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 re...

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Bibliographic Details
Published in:PLoS computational biology 2024-05, Vol.20 (5), p.e1012094
Main Authors: Koptagel, Hazal, Jun, Seong-Hwan, Hård, Joanna, Lagergren, Jens
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Cancer
Cell differentiation
Cell lineage
Cell Lineage - genetics
Computational Biology - methods
Datasets
Deoxyribonucleic acid
DNA
DNA sequencing
Dynamic programming
Gene sequencing
Genomes
Methods
Models, Statistical
Mutation
Nucleotide sequencing
Phylogenetics
Probability
Random variables
Reconstruction
Sequence Analysis, DNA - methods
Single-Cell Analysis - methods
Site selection
Software
Synthetic data
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Description
Summary: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 method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1012094