bio-gene-regulatory-networks-perturbation-simulation

CellOracle simulates transcription factor perturbations by integrating a base GRN derived from accessible chromatin with scRNA-seq expression data. It fits regression models per cell type to predict how TF knockouts or overexpression shift cell identities and trajectories.

You need preprocessed AnnData containing clustering and embedding information, plus a base chromatin-derived GRN. A Python environment with CellOracle installed is required to construct, fit, and perturb GRN models for cell state predictions.

Yes, scanpy preprocessed AnnData containing clustering and embedding information is compatible with CellOracle GRN modeling. The Skill utilizes scanpy as a dependency to support the preprocessing required for perturbation simulations.

Prioritize TF perturbations for perturb-seq planning by ranking transcription factors by their predicted impact on cell state shifts. This Skill simulates knockout and overexpression effects across multiple cell types to identify driver TFs for cell fate transitions.

Integrating a chromatin-derived base GRN with scRNA-seq expression grounds perturbation simulations in physical TF-target links. This combined approach allows the model to accurately predict how knockouts or overexpression alter cell identities and map embedding shifts.

The accuracy of simulating overexpression effects across multiple cell types depends entirely on the quality of the prerequisite inputs. The Skill requires a properly preprocessed AnnData with clustering and a compatible base chromatin-derived GRN to fit valid regression models.