RT Journal
A1 Zhang, Haotian
A1 Zhang, Yiming
A1 Gao, Teng
A1 Wu, Yufeng
T1 ScisTree2 enables large-scale inference of cell lineage trees and genotype calling using efficient local search
JF Genome Research 
JO Genome Research 
YR 2025 
FD December 01 
VO 35 
IS 12 
SP 2781 
OP 2791 
DO 10.1101/gr.280542.125 
UL http://genome.cshlp.org/content/35/12/2781.abstract 
AB In a multicellular organism, cell lineages share a common evolutionary history. Knowing this history can facilitate the study of development, aging, and cancer. Cell lineage trees represent the evolutionary history of cells sampled from an organism. Recent developments in single-cell sequencing have greatly facilitated the inference of cell lineage trees. However, single-cell data are sparse and noisy, and the size of single-cell data is increasing rapidly. Accurate inference of cell lineage tree from large single-cell data is computationally challenging. In this paper, we present ScisTree2, a fast and accurate cell lineage tree inference and genotype calling approach based on the infinite-sites model. ScisTree2 relies on an efficient local search approach to find optimal trees. ScisTree2 also calls single-cell genotypes based on the inferred cell lineage tree. Experiments on simulated and real biological data show that ScisTree2 achieves better overall accuracy while being significantly more efficient than existing methods. To the best of our knowledge, ScisTree2 is the first model-based cell lineage tree inference and genotype calling approach that is capable of handling data sets from tens of thousands of cells or more.