Fig. 1

A PMF-GRN graphical model overview. Input single-cell gene expression W is decomposed into several latent factors. Information obtained from chromatin accessibility data or genomics databases is incorporated into the prior distribution for A. B Input experimental data for PMF-GRN includes single-cell RNA-seq gene expression data. Prior-known TF-target gene interactions can be obtained using chromatin accessibility in parallel with known TF motifs or through databases or literature derived interactions. C Hyperparameter selection process is performed for optimal model selection. The provided prior-known network is split into a train and validation dataset. 80% of the prior-known information is used to infer a GRN, while the remaining 20% is used for validation by computing AUPRC. This process is repeated multiple times, using different hyperparameter configurations in order to determine the optimal hyperparameters for the GRN inference task at hand. Finally, using the optimal hyperparameters, a final network is inferred using the full prior and evaluated using an independent gold standard