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

• DOI: 10.1038/s41467-024-50194-3
• Publication Date: 2024-07-16T15:09:43Z
• Authors: Zehua Zeng; Yuqing Ma; Lei Hu; Bowen Tan; Peng Liu; Yixuan Wang; Cencan Xing; Yuanyan Xiong; Hongwu Du
• Keywords: 0301 basic medicine; Sequence Analysis, RNA; Science; Q; Computational Biology; High-Throughput Nucleotide Sequencing; Article; 03 medical and health sciences; Humans; RNA-Seq; Neural Networks, Computer; Single-Cell Analysis; Algorithms; Software

AbstractSingle-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.