美国弗吉尼亚理工大学计算机科学系T. M. Murali研究组建立从单细胞转录组数据中推断基因调控网络的基准化算法。2020年1月6日，《自然—方法学》在线发表了这一成果。

研究介绍了从单细胞转录数据推断基因调控网络的最新算法的系统评价。作为评估准确性的基础，使用具有可预测轨迹的合成网络、文献策划的布尔模型和各种转录调控网络。他们开发了一种方法，可以模拟来自合成和布尔网络的单细胞转录数据，从而避免了先前使用方法的陷阱。

此外，他们从多个实验性单细胞RNA-seq数据集来收集网络，开发了一个称为BEELINE的评估框架。他们发现该算法的精确度调用曲线下的面积和早期精确度是适中的。该方法在恢复合成网络中的交互方面比布尔模型更好。布尔模型具有最佳早期精度值的算法在实验数据集上也应用良好。不需要伪时间排序单元的技术通常更准确。基于这些结果，研究人员向最终用户提出了建议。BEELINE将帮助开发基因调控网络推论算法。

附：英文原文

Title: Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Author: Aditya Pratapa, Amogh P. Jalihal, Jeffrey N. Law, Aditya Bharadwaj, T. M. Murali

Issue&Volume: 2020-01-06

Abstract: We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms.

DOI: 10.1038/s41592-019-0690-6

Source: https://www.nature.com/articles/s41592-019-0690-6