Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples
Fig 2
Demonstrating robustness of PROB using synthetic datasets at different levels of variabilities.
A set of expression data for 6 genes in 100 cancer patients was simulated. Different levels of technical variabilities (with coefficient of variations (CVs) = 0%, 5%, 10% and 15% respectively) were introduced into the progression-dependent gene expression dynamics. (a) Simulated cross-sectional gene expression data. The sample IDs of the synthetic data were randomized and the staging information was retained. (b) Comparison of the inferred latent-temporal progression with the true progression in the synthetic dataset, evaluated using Spearman’s rank correlation coefficient (rho). (c) Recovered gene expression dynamics according to inferred progression trajectory. (d) Accuracy of the GRN inference evaluated using the areas under curve (AUCs) of the ROCs.