Journal
ANNALS OF APPLIED STATISTICS
Volume 15, Issue 1, Pages 343-362Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/20-AOAS1400
Keywords
Phylogenetics; tree reconstruction; cell lineage tracing; maximum likelihood estimator; regularization
Categories
Funding
- National Institutes of Health [R01-GM113246, R01-AI146028]
- National Science Foundation [CISE1564137]
- Howard Hughes Medical Institute
- Simons Foundation
- NIH Early Independence Award [5DP5OD019820]
- NIH [5T32HG000035-23, F31 AI150163]
- NIH/NHGRI Pathway to Independence Award [K99HG010152/R00HG010152]
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CRISPR technology enables cell lineage tracing in complex multicellular organisms by using insertion-deletion mutations of synthetic genomic barcodes. Researchers have proposed a statistical model and developed a procedure to estimate tree topology, branch lengths, and mutation parameters. Their method infers relative ordering across parallel lineages, offering advantages over existing techniques.
CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from (Science 353 (2016) aaf7907), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.
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