Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R

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).