gene-regulatory-networks

To infer gene regulatory networks from expression data, provide an expression matrix and optional transcription factor priors or chromatin features to generate inferred networks, regulon activity tables, and network visualizations.

Regulon activity scoring in regulatory genomics analyses quantifies the collective expression of target genes controlled by a transcription factor, allowing you to interpret transcriptional programs and their dynamics in response to treatments or perturbations.

Yes, you can use optional chromatin features and transcription factor priors alongside your expression matrix to improve gene regulatory network inference and generate more accurate regulon activity tables and visualizations.

To visualize transcription factor networks for perturbation-aware comparisons, input your expression matrix to generate regulator-centric views and network visualizations that reveal regulatory programs and their dynamics across different conditions.

Generating gene regulatory networks and regulon activity tables requires an expression matrix as the primary input, while optional transcription factor priors and chromatin features can be provided to refine the inferred networks and visualizations.