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 () and Hongwu Du ()
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Zehua Zeng: University of Science and Technology Beijing
Yuqing Ma: Tsinghua-Berkeley Shenzhen Institute
Lei Hu: University of Science and Technology Beijing
Bowen Tan: Chinese Academy of Sciences
Peng Liu: University of Science and Technology Beijing
Yixuan Wang: University of Science and Technology Beijing
Cencan Xing: University of Science and Technology Beijing
Yuanyan Xiong: Guangzhou
Hongwu Du: University of Science and Technology Beijing

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50194-3

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