Evidence map›Paper›PMID 35412632›Full record

ArticleNucleic acids research2022

TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history.

Xinhai Pan, Hechen Li, Xiuwei Zhang

Abstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

3 authors.

Xinhai PanSchool of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Hechen LiSchool of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Xiuwei ZhangSchool of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0002-1713-772X

Funding

Studying temporal dynamics and regulatory mechanisms of single cells with a unified framework and multi-omics dataR35GM143070 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI ZHANG, XIUWEI · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM143070
6 · The paper itself

Abstract

Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.

Indexed as

CRISPR-Cas SystemsSingle-Cell AnalysisCell DivisionCell LineageSequence Analysis, RNA

Identifiers

PMID35412632
PMCPMC9071466

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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.