Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples.

ABSTRACT: Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically

SUBMITTER: Sun X 

PROVIDER: S-EPMC7968745 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature