Evidence map›Paper›PMID 35990087›Full record

ArticleThe annals of applied statistics2021

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

Jean Feng, William S Dewitt, Aaron McKenna, Noah Simon, Amy D Willis, Frederick A Matsen

Abstract read
In one paragraph

Article in The annals of applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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  5. Article
  6. Review
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  11. Article
  12. Inferring Cell Differentiation Dynamics with Unobserved Progenitors.bioRxiv : the preprint server for biology · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. The lives of cells, recorded.Nature reviews. Genetics · 2025
    Review
  18. A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jean FengDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
William S DewittDepartment of Genome Sciences, University of Washington.
Aaron McKennaDepartment of Molecular and Systems Biology, Dartmouth College.
Noah SimonDepartment of Biostatistics, University of Washington.
Amy D WillisDepartment of Biostatistics, University of Washington.
Frederick A MatsenComputational Biology Program, Fred Hutchinson Cancer Research Center.

Funding

INTERDISCIPLINARY TRAINING IN GENOMIC SCIENCEST32HG000035 · NHGRI · UNIVERSITY OF WASHINGTON · PI Bruce Colston Trapnell · 1995 to 2026
$24.2M
Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptorsR01AI146028 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2019 to 2024
$3.4M
Leveraing deep sequencing data to understand antibody maturationR01GM113246 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2014 to 2018
$1.9M
Data-Driven Statistical Learning with Applications to GenomicsDP5OD019820 · OD · UNIVERSITY OF WASHINGTON · PI SIMON, NOAH · 2014 to 2018
$1.7M
Multidimensional cell recording with single-cell genomicsR00HG010152 · NHGRI · DARTMOUTH COLLEGE · PI MCKENNA, AARON H · 2019 to 2021
$729k
Evolutionary dynamics of antibody affinity maturationF31AI150163 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI DEWITT, WILLIAM S. · 2020 to 2022
$113k
Multidimensional cell recording with single-cell genomicsK99HG010152 · NHGRI · UNIVERSITY OF WASHINGTON · PI MCKENNA, AARON H · 2018 to 2018
$97k
NHGRI NIH HHS K99 HG010152NHGRI NIH HHS R00 HG010152NHGRI NIH HHS T32 HG000035NIAID NIH HHS F31 AI150163NIAID NIH HHS R01 AI146028NIGMS NIH HHS R01 GM113246NIH HHS DP5 OD019820
6 · The paper itself

Abstract

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 McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Identifiers

PMID35990087
PMCPMC9387344

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.