Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.
Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.
复制标题
转录因子流形上的结构引物嵌入使基因调控网络和潜在活性推断的透明模型架构成为可能。
DOI：
10.1186/s13059-023-03134-1
复制
发表时间：
2024-01-18
期刊：
影响因子：
12.3
通讯作者：
中科院分区：
文献类型：
--
作者：
由于缺乏对全基因组转录因子活性（TFA）的直接测量，基因调控网络（GRNs）的建模受到限制，这使得难以分离协方差和调控相互作用。监管互动和TFA的推断需要补充证据的汇总。明确估计TFA是有问题的，因为它断开GRN推断和TFA估计，并且不能解释例如上下文转录因子-转录因子相互作用和其他更高阶特征。深度学习提供了一个潜在的解决方案，因为它可以对复杂的交互和高阶潜在特征进行建模，尽管它没有提供可解释的模型和潜在特征。我们提出了一种新的自动编码器为基础的框架，结构启动推理的监管使用潜在的因素活性（SupirFactor）建模，和一个度量，解释相对方差（ERV），解释GRNs。我们评估SupirFactor与ERV在广泛的背景下。与当前最先进的GRN推理方法相比，SupirFactor表现良好。我们评估潜在特征活性作为S.酿酒酵母以及外周血单个核细胞（PBMC）中。在这里，我们提出了一个框架，结构引发的推理和解释的GRNs，SupirFactor，演示可解释性使用ERV在多个生物和实验设置。SupirFactor可以使用潜在因子活性进行TFA估计和途径分析，这里在两个大规模单细胞数据集上进行了演示，模拟S。酿酒酵母和PBMC。我们发现，SupirFactor模型有利于生物分析获得新的功能和监管的见解。在线版本包含补充材料，可通过10.1186/s13059-023-03134-1获得。
Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features. We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC). Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight. The online version contains supplementary material available at 10.1186/s13059-023-03134-1.
登录
查看更多内容
影响因子：
9.9
作者：
Arrieta-Ortiz ML;Hafemeister C;Bate AR;Chu T;Greenfield A;Shuster B;Barry SN;Gallitto M;Liu B;Kacmarczyk T;Santoriello F;Chen J;Rodrigues CD;Sato T;Rudner DZ;Driks A;Bonneau R;Eichenberger P
通讯作者：
Eichenberger P
影响因子：
3.7
作者：
Madar A;Greenfield A;Vanden-Eijnden E;Bonneau R
通讯作者：
Bonneau R
DOI：
10.1126/science.abc6261
发表时间：
2020-09-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Arunachalam PS;Wimmers F;Mok CKP;Perera RAPM;Scott M;Hagan T;Sigal N;Feng Y;Bristow L;Tak-Yin Tsang O;Wagh D;Coller J;Pellegrini KL;Kazmin D;Alaaeddine G;Leung WS;Chan JMC;Chik TSH;Choi CYC;Huerta C;Paine McCullough M;Lv H;Anderson E;Edupuganti S;Upadhyay AA;Bosinger SE;Maecker HT;Khatri P;Rouphael N;Peiris M;Pulendran B
通讯作者：
Pulendran B
影响因子：
16.6
作者：
Danese A;Richter ML;Chaichoompu K;Fischer DS;Theis FJ;Colomé-Tatché M
通讯作者：
Colomé-Tatché M
影响因子：
2.5
作者：
GOLUB, GH;HEATH, M;WAHBA, G
通讯作者：
WAHBA, G