Evidence map›Paper›PMID 39013860›Full record

ArticleNature communications2024

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.

Zehua Zeng, Yuqing Ma, Lei Hu, Bowen Tan, Peng Liu, Yixuan Wang, Cencan Xing, Yuanyan Xiong, Hongwu Du

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing 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

62 citing papers in PubMed.

  1. Article
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  5. APOEScience advances · 2026
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  7. Integrated Single-Cell and TCR Profiling Reveals Protection-Associated CD8Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  8. Article
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2 more citing papers are in PubMed but not listed here.

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

9 authors.

Zehua Zeng *School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China. starlitnightly@gmail.com.ORCID 0000-0002-2109-0418
Yuqing Ma *Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong Province, China.
Lei Hu *School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.ORCID 0009-0005-5885-6711
Bowen TanAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.ORCID 0009-0009-7881-311X
Peng LiuSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
Yixuan WangSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
Cencan XingSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China. cencanxing@ustb.edu.cn.ORCID 0000-0002-0731-4809
Yuanyan XiongKey Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun-Yat-Sen University, Guangzhou, Guangdong, China. xyyan@mail.sysu.edu.cn.ORCID 0000-0002-3227-5215
Hongwu DuSchool of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China. hongwudu@ustb.edu.cn.ORCID 0000-0002-9809-5313

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32300682
6 · The paper itself

Abstract

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Indexed as

AlgorithmsSequence Analysis, RNASingle-Cell AnalysisComputational BiologyHigh-Throughput Nucleotide SequencingHumansNeural Networks, ComputerRNA-SeqSoftware

Identifiers

PMID39013860
PMCPMC11252408

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.