School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.
School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.
STAR Protoc. 2022 Jun 14;3(3):101467. doi: 10.1016/j.xpro.2022.101467. eCollection 2022 Sep 16.
Due to a lack of explicit temporal information, it can be challenging to infer gene regulatory networks from clinical transcriptomic data. Here, we describe the protocol of PROB_R for inferring latent temporal disease progression and reconstructing gene regulatory networks from cross-sectional clinical transcriptomic data. We illustrate the protocol by applying it to a breast cancer dataset to demonstrate its use in recovering pseudo-temporal dynamics of gene expression alongside disease progression, reconstructing gene regulatory networks, and identifying key regulatory genes. For complete details on the use and execution of this protocol, please refer to Sun et al. (2021).
由于缺乏明确的时间信息，从临床转录组数据推断基因调控网络具有一定的挑战性。在这里，我们描述了 PROB_R 从横向临床转录组数据推断潜在的时间疾病进展和重建基因调控网络的协议。我们通过将其应用于乳腺癌数据集来说明该协议的使用，以展示其在恢复基因表达的伪时间动态以及重建基因调控网络和识别关键调控基因方面的用途。有关该协议的使用和执行的完整详细信息，请参考 Sun 等人（2021 年）。
