转录调控在决定细胞命运和疾病方面发挥着至关重要的作用，但从基因表达数据推断关键调控因子仍然是一个重大挑战。现有的估计转录因子 (TF) 活性的方法通常依赖于静态 TF-基因相互作用数据库，无法适应不同细胞类型和疾病条件下调节机制的变化。在这里，我们提出了一种新算法 - 使用基因表达和调控数据的转录推理 (TIGER) - 通过灵活地建模激活和抑制事件、增加基本边缘的权重、通过稀疏贝叶斯先验将不相关的边缘缩小到零来克服这些限制，以及同时估计 TF 活动水平和基础监管网络的变化。当应用于酵母和癌症 TF 敲除数据集时，TIGER 在预测准确性方面优于同类方法。此外，我们将 TIGER 应用于组织和细胞类型特异性 RNA-seq 数据，证明了其揭示调控机制差异的能力。总的来说，我们的研究结果强调了在推断转录因子活动时对特定环境调节进行建模的实用性。© 2024。作者。

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.© 2024. The Author(s).