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

Author

Listed:
  • Zehua Zeng

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

  • Yuqing Ma

    (Tsinghua-Berkeley Shenzhen Institute
    Tsinghua Shenzhen International Graduate School)

  • Lei Hu

    (University of Science and Technology Beijing
    Westlake University)

  • Bowen Tan

    (Chinese Academy of Sciences
    University of Science and Technology Beijing)

  • Peng Liu

    (University of Science and Technology Beijing)

  • Yixuan Wang

    (University of Science and Technology Beijing)

  • Cencan Xing

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

  • Yuanyan Xiong

    (Guangzhou)

  • Hongwu Du

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

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.

Suggested Citation

  • Zehua Zeng & Yuqing Ma & Lei Hu & Bowen Tan & Peng Liu & Yixuan Wang & Cencan Xing & Yuanyan Xiong & Hongwu Du, 2024. "OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50194-3
    DOI: 10.1038/s41467-024-50194-3
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    as
    1. Jie Liao & Jingyang Qian & Yin Fang & Zhuo Chen & Xiang Zhuang & Ningyu Zhang & Xin Shao & Yining Hu & Penghui Yang & Junyun Cheng & Yang Hu & Lingqi Yu & Haihong Yang & Jinlu Zhang & Xiaoyan Lu & Li , 2022. "De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Yanshuo Chen & Yixuan Wang & Yuelong Chen & Yuqi Cheng & Yumeng Wei & Yunxiang Li & Jiuming Wang & Yingying Wei & Ting-Fung Chan & Yu Li, 2022. "Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Shobana V. Stassen & Gwinky G. K. Yip & Kenneth K. Y. Wong & Joshua W. K. Ho & Kevin K. Tsia, 2021. "Generalized and scalable trajectory inference in single-cell omics data with VIA," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    4. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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