Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions.
Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions.
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DOI：
10.1093/bib/bbad370
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发表时间：
2023-09-22
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
基因调控网络（grn）驱动着生物体的结构和功能，因此grn的发现和表征是生物学研究的主要目标。然而，使用基因表达数据集（最近来自单细胞RNA-seq (scRNA-seq)）准确识别grn的因果调节联系和推断一直具有挑战性。在这里，我们采用基于交易组合的因果推理（CICT）的创新方法从scRNA-seq数据中发现grn。CICT的基础是，如果所有的基因表达都是随机的，那么一个非随机的调控基因在不同于背景随机过程的水平上诱导其靶标，从而导致整个基因-基因关联的相关网络呈现出不同的模式。CICT提出了源自关联网络的新颖网络特征，使任何机器学习算法都能够预测因果调节边并推断grn。我们在一个完善的基准测试管道中使用模拟和实验的scRNA-seq数据对CICT进行了评估，结果表明，CICT优于现有的网络推理方法，代表了多种方法，准确率提高了许多倍。此外，我们证明了使用CICT的GRN推理对scRNA-seq数据的不同稀疏度、数据和基础真值的特征、关联度量的选择以及监督机器学习算法的复杂性都具有鲁棒性。我们的研究结果表明，旨在直接预测因果关系以恢复复杂生物网络中的调节关系可以大大提高GRN推理的准确性。
Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene–gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.
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DOI：
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