ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS

• 成果类型: Article
• 署名作者: Feng, Jean; DeWitt, William S., III; McKenna, Aaron; Simon, Noah; Willis, Amy D.; Matsen, Frederick A.
• 署名单位: University of California System; University of California San Francisco; University of Washington; University of Washington Seattle; Dartmouth College; University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1400
• 发表日期: 2021
• 页码: 343-362
• 关键词: aggregation; SEQUENCES; inference

摘要

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.