Joint gene network construction by single-cell RNA sequencing data.
Joint gene network construction by single-cell RNA sequencing data.
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DOI：
10.1111/biom.13645
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发表时间：
2023-06
期刊：
影响因子：
1.9
通讯作者：
Zou, Fei
中科院分区：
文献类型：
--
作者：
Dong, Meichen;He, Yiping;Jiang, Yuchao;Zou, Fei
与单基因水平的差异基因表达分析相反，基因调控网络（GRN）分析描绘了基因之间复杂的转录组相互作用，以更好地理解人类疾病和性状的潜在遗传结构。单细胞RNA测序（scRNA-seq）的最新进展允许以比批量RNA-seq和微阵列数据更精细的分辨率构建GRNs。然而，scRNA-seq数据本质上是稀疏的，这阻碍了流行的高斯图形模型（GGM）的直接应用。此外，大多数现有的利用scRNA-seq数据构建GRNs的方法只考虑一种条件下的基因网络。为了更好地理解单细胞分辨率下不同但相关条件下的GRNs，我们建议在GGM框架下构建具有scRNA-seq数据的联合基因网络（JGNsc）。为了便于使用GGM，JGNsc首先提出了一种混合插补程序，该程序将贝叶斯零膨胀泊松模型与迭代低秩矩阵完成步骤相结合，以有效地插补技术伪影导致的零膨胀计数。然后，JGNsc通过非超常变换来变换插补数据，基于该非超常变换来构造联合GGM。我们展示了JGNsc，并使用合成数据评估其性能。JGNsc在髓母细胞瘤和胶质母细胞瘤两项癌症临床研究中的应用，除了证实众所周知的生物学结果外，还获得了新的见解。
In contrast to differential gene expression analysis at the single-gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recent advances in single-cell RNA sequencing (scRNA-seq) allow constructing GRNs at a much finer resolution than bulk RNA-seq and microarray data. However, scRNA-seq data are inherently sparse, which hinders the 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 across different but related conditions at single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) under the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma gains novel insights in addition to confirming well-known biological results.
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