Background: The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results: Using a new dataset, we systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. These approaches require a TF network map, which specifies the target genes of each TF, as input. We evaluate different approaches to building the network map and deriving constraints on the matrices. We find that such constraints are essential for good performance. Constraints can be obtained from expression data in which the activities of individual TFs have been perturbed, and we find that such data are both necessary and sufficient for obtaining good performance. Remaining uncertainty about whether a TF activates or represses a target is a major source of error. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions. As a result, the control strength matrices derived here can be used for other applications. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of four yeast TFs: Gcr2, Gln3, Gcn4, and Msn2. Evaluation code and data available at https://github.com/BrentLab/TFA-evaluation Conclusions: When a high-quality network map, constraints, and perturbation-response data are available, inferring TF activity levels by factoring gene expression matrices is effective. Furthermore, it provides insight into regulators of TF activity.

中文翻译：

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从TF扰动数据的约束条件下根据基因表达数据推断TF活性和活性调节剂

背景：细胞样品中转录因子（TF）的活性是其发挥调节潜力的程度。已经描述了许多从基因表达数据推断TF活性的方法，但是由于缺乏适当的大规模数据集，到目前为止，尚不可能进行系统和客观的验证。结果：使用新的数据集，我们系统地评估和优化了TF活性推断的方法，在该方法中，基因表达矩阵被分解为控制强度的与条件无关的矩阵和TF活性水平的与条件无关的矩阵。这些方法需要一个TF网络图，该图指定每个TF的靶基因作为输入。我们评估构建网络图和推导矩阵约束的不同方法。我们发现这样的约束对于良好的性能至关重要。可以从表达数据中获取约束，其中单个TF的活动受到干扰，我们发现此类数据对于获得良好的性能既必要又充分。关于TF是激活还是压制目标的不确定性仍然是主要的错误来源。在相当程度上，使用来自一种生长条件的表达数据推断出的控制强度会延续到其他条件。结果，这里导出的控制强度矩阵可以用于其他应用。最后，我们应用这些方法来深入了解调控四个酵母TF的上游因子：Gcr2，Gln3，Gcn4和Msn2。评估代码和数据可在https://github.com/BrentLab/TFA-evaluation结论中找到：当可获得高质量的网络图，约束条件和摄动响应数据时，通过分解基因表达矩阵来推断TF活性水平是有效的。此外，它提供了对TF活动调节剂的见解。