bioRxiv preprint doi: https://doi.org/10.1101/2021.07.14.452387; this version posted July 14, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Biometrics xx, 1–24 DOI: pending...... xxxx 2021 Joint Gene Network Construction by Single-Cell RNA Sequencing Data Meichen Dong1, Yiping He2, Yuchao Jiang 1,3, Fei Zou1,3,∗ 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A. 2Department of Pathology, Duke University, Durham, North Carolina, U.S.A. 3Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A. email:
[email protected] Summary: In contrast to differential gene expression analysis at single gene level, gene regulatory networks (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recently, single-cell RNA sequencing (scRNA-seq) data has started to be used for constructing GRNs at a much finer resolution than bulk RNA-seq data and microarray data. However, scRNA- seq data are inherently sparse which hinders direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the GGMs framework.