Maximum Likelihood Inference of Time-Scaled Cell Lineage Trees with Mixed-Type Missing Data

Uyen Mai, Gillian Chu, Benjamin J. Raphael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key problem in lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Several features of lineage tracing data distinguish this problem from standard phylogenetic tree inference: CRISPR-induced mutations are non-modifiable and can result in distinct sets of possible mutations at each target site; the number of mutations decreases over time due to non-modifiability; and CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to compute a maximum likelihood tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings
EditorsJian Ma
PublisherSpringer Science and Business Media Deutschland GmbH
Pages360-363
Number of pages4
ISBN (Print)9781071639887
DOIs
StatePublished - 2024
Event28th International Conference on Research in Computational Molecular Biology, RECOMB 2024 - Cambridge, United States
Duration: Apr 29 2024May 2 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14758 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Research in Computational Molecular Biology, RECOMB 2024
Country/TerritoryUnited States
CityCambridge
Period4/29/245/2/24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • cell phylogeny inference
  • evolutionary model
  • maximum likelihood

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