Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.
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DOI：
10.1186/s12859-018-2217-z
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发表时间：
2018-06-19
影响因子：
3
通讯作者：
Mar JC
中科院分区：
文献类型：
--
作者：
Chen S;Mar JC
生物学中的一个基本事实表明，基因并不是孤立地运作的，然而，为单细胞基因表达数据推断调控网络的方法出现得很慢。随着单细胞测序方法现在变得可用，最初为从批量样本收集的数据开发的通用网络推理算法可能不适用于单个细胞。同时，尽管专门针对单细胞数据的方法正在涌现，但它们是否比一般方法有更好的性能尚不清楚。在这项研究中，我们评估了五种通用方法和三种单细胞方法在从实验单细胞基因表达数据和计算机模拟数据中推断基因调控网络的适用性。使用ROC曲线和Precision-Recall曲线对来自文献的参考集的标准评估指标表明，大多数方法在应用于实验单细胞数据或模拟单细胞数据时表现不佳，这表明它们在这项任务中缺乏性能。使用默认设置时，网络方法被应用于相同的数据集。学习的网络的比较突出了每种方法的一些预测边的唯一性。不同的方法可以推断出不同的网络，这一事实实质上反映了将网络方法彼此区分开来的基本数学原理和假设。这项研究对网络建模算法进行了全面的评估，这些算法应用于实验单细胞基因表达数据和已知网络结构的计算机模拟数据集中。比较表明，这些被评估的网络方法中的大多数都不能从单细胞表达数据中准确地预测网络结构，即使它们是专门为单细胞方法开发的。而且，通常依赖于更复杂的算法的单细胞方法在检测到的边缘集合中通常彼此具有较少的相似性。这项研究的结果强调了开发更准确的、与单元格数据兼容的优化网络建模方法的重要性。新开发的单细胞方法可能独特地捕捉潜在基因-基因关系的特定特征，当我们解释这些结果时应谨慎。本文的在线版本(10.1186/s12859-0182217-z)包含补充材料，可供授权用户使用。
A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results. The online version of this article (10.1186/s12859-018-2217-z) contains supplementary material, which is available to authorized users.
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影响因子：
3.7
作者：
Dal Molin A;Baruzzo G;Di Camillo B
通讯作者：
Di Camillo B
综合的单细胞数据分析揭示了细胞特异性网络和新颖的共激活标记。
影响因子：
--
作者：
Ghazanfar S;Bisogni AJ;Ormerod JT;Lin DM;Yang JY
通讯作者：
Yang JY
影响因子：
4.3
作者：
Campbell KR;Yau C
通讯作者：
Yau C
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
48
作者：
Kharchenko, Peter V.;Silberstein, Lev;Scadden, David T.
通讯作者：
Scadden, David T.