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Cited by 102Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

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

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Zeng, Zehua, Ma, Yuqing, Hu, Lei, Tan, Bowen, Liu, Peng, Wang, Yixuan, Xing, Cencan, Xiong, Yuanyan +1 more·2024·Nature Communications
DOI 10.1038/s41467-024-50194-3PMC PMC11252408

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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.

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  • To integrate bulk and single-cell transcriptome data, researchers have employed the β-VAE method to deconvolute bulk data into cell-type-specific expression profiles, addressing the 'omission' issue in single-cell sequencing.

    “To integrate bulk and single-cell transcriptome data, researchers have employed the β-VAE method to deconvolute bulk data into cell-type-specific expression profiles, addressing the ‘omission’ issue in single-cell sequencing [173].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [173]

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Authors
Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wang, Y., Xing, C., Xiong, Y., & Du, H.
Affiliations
  • University of Science and Technology Beijing (CN)
  • Tsinghua–Berkeley Shenzhen Institute (CN)
  • Westlake University (CN)
  • Chinese Academy of Sciences (CN)
  • Sun Yat-sen University (CN)
Corresponding author
  • Zeng, Zehua
  • Xing, Cencan
  • Xiong, Yuanyan
  • Du, Hongwu
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-07-16
Volume
15
Issue
1
Pages
5983-5983
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-50194-3
PMC
PMC11252408
OpenAlex
W4400683630
Cited by
102
References
75
Citations by year
2026: 41 · 2025: 49 · 2024: 10 · 2023: 1 · 2022: 1
FWCI
13.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
National Natural Science Foundation of China (32300682)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
bulk RNA-seq, single-cell RNA sequencing, beta-VAE, graph neural networks, community detection, multi-omics integration
MeSH terms
RNA-Seq, Algorithms, Humans, Software, Neural Networks, Computer, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis, High-Throughput Nucleotide Sequencing

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