PT  - JOURNAL ARTICLE
AU  - Jean Feng
AU  - William S DeWitt III
AU  - Aaron McKenna
AU  - Noah Simon
AU  - Amy Willis
AU  - Frederick A Matsen IV
TI  - Estimation of cell lineage trees by maximum-likelihood phylogenetics
AID  - 10.1101/595215
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 595215
4099  - http://biorxiv.org/content/early/2019/03/31/595215.short
4100  - http://biorxiv.org/content/early/2019/03/31/595215.full
AB  - 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.