2019/03/29 by Jean Feng, William S. DeWitt, Feng, Jean +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Cancer Genomics and Diagnostics #FOS: Biological sciences #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.1904.00117
openalex publication_date 2019/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, these sophisticated biological tools currently use ad-hoc and outmoded computational methods to reconstruct the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the biological mechanism, they are unable to take full advantage of the data's structure. We propose a statistical model for the mutation process and develop a procedure to estimate the tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. In contrast to existing techniques, our method estimates time along each branch, rather than number of mutation events, thus providing a detailed account of tissue-type differentiation. Via simulations, we demonstrate that our method is substantially more accurate than existing approaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development and reproduce similar results across fish replicates.