Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.
Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.
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DOI：
10.1186/s12859-023-05277-1
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发表时间：
2023-04-18
影响因子：
3
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中科院分区：
文献类型：
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阐明化合物的作用机制（MoA）有利于药物发现，但在实践中往往是一个重大的挑战。因果推理方法旨在通过使用转录组学数据和生物网络推断失调的信号传导蛋白来解决这种情况;然而，尚未报道这种方法的全面基准。在这里，我们使用LINCS L1000和CMap微阵列数据，用四个网络（较小的Omnipath网络与3个较大的MetaBase™网络）对四种因果推理算法（SigNet，CaClIR，CaClIR ScanR和CARNIVAL）进行了基准测试，并评估了每个因素在多大程度上决定了在包含269种化合物的基准数据集中成功恢复直接靶标和化合物相关信号传导途径。我们还研究了蛋白质靶点的功能和作用及其在先验知识网络中的连接偏差对性能的影响。根据统计分析（负二项模型），算法和网络的组合最显着地决定了因果推理算法的性能，SigNet恢复了最多的直接目标。关于信号通路的恢复，基于Reactome通路层次结构，CARNIVAL与Omnipath网络能够恢复包含化合物靶标的最具信息性的通路。此外，CARNIVAL、SigNet和CairdR ScanR均优于基线基因表达途径富集结果。我们发现L1000数据或微阵列数据之间的性能没有显着差异，即使仅限于978个“地标”基因。值得注意的是，所有因果推理算法也优于基于输入DEG的途径恢复，尽管这些经常用于途径富集。因果推理方法的性能与目标的连接性和生物学作用有一定的相关性。总体而言，我们的结论是因果推理在恢复信号蛋白相关的化合物MoA上游基因表达的变化，利用先验知识网络，以及网络和算法的选择有着深远的影响因果推理算法的性能。基于本文所述的分析，这对于基于微阵列的基因表达数据以及基于L1000平台的基因表达数据都是正确的。在线版本包含补充材料，可通过10.1186/s12859-023-05277-1获得。
Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks. According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 ‘landmark’ genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets. Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform. The online version contains supplementary material available at 10.1186/s12859-023-05277-1.
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影响因子：
3
作者：
Catlett NL;Bargnesi AJ;Ungerer S;Seagaran T;Ladd W;Elliston KO;Pratt D
通讯作者：
Pratt D
影响因子：
14.9
作者：
Gillespie M;Jassal B;Stephan R;Milacic M;Rothfels K;Senff-Ribeiro A;Griss J;Sevilla C;Matthews L;Gong C;Deng C;Varusai T;Ragueneau E;Haider Y;May B;Shamovsky V;Weiser J;Brunson T;Sanati N;Beckman L;Shao X;Fabregat A;Sidiropoulos K;Murillo J;Viteri G;Cook J;Shorser S;Bader G;Demir E;Sander C;Haw R;Wu G;Stein L;Hermjakob H;D'Eustachio P
通讯作者：
D'Eustachio P
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
9.9
作者：
通讯作者：
--
DOI：
10.1002/wsbm.114
发表时间：
2011-03
影响因子：
7.9
作者：
Berger, Seth I.;Iyengar, Ravi
通讯作者：
Iyengar, Ravi