Evidence map›Paper›PMID 36590685›Full record

ArticleCell reports methods2022

Simulation-based inference of differentiation trajectories from RNA velocity fields.

Revant Gupta, Dario Cerletti, Gilles Gut, Annette Oxenius, Manfred Claassen

Abstract read
In one paragraph

Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
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  4. Observational
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  6. Article
  7. Review
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  9. Article
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  12. Hodge Decomposition of Single-Cell RNA Velocity.Journal of chemical information and modeling · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. RNA velocity unraveled.PLoS computational biology · 2022
    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

5 authors.

Revant GuptaInternal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany.
Dario CerlettiInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Gilles GutInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Annette OxeniusInstitute of Microbiology, ETH Zurich, Zurich, Switzerland.
Manfred ClaassenInternal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis, we include differentiation trajectories with varying bifurcated, circular, convergent, and mixed topologies studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate the capability to reconstruct differentiation trajectories, assess the association of RNA velocity-based pseudotime with actually elapsed process time, and identify drawbacks in current state-of-the art trajectory inference approaches.

Indexed as

RNACell DifferentiationComputer SimulationMarkov ChainsRNARNA velocitysimulation-based inferencesingle-cell RNA sequencingtrajectory inference

Identifiers

PMID36590685
PMCPMC9795361

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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.