Predicting and Interpreting Cell-type Specific Drug Responses in the Low data regime using Inductive Priors

PrePR-CT is a graph-based deep learning method designed to predict transcriptional responses to chemical perturbations in single-cell data. This method utilizes Graph Attention Network (GAT) layers to encode cell-type graphs from batches of training samples. These encoded graphs are then integrated with control gene expression data and predefined perturbation embeddings. The combined data is processed through Multi-Layer Perceptrons (MLPs) to accurately predict gene expression responses. We have included the Kang dataset, which is essential for reproducing the results presented in the paper. Alongside the dataset, we provide a notebook example that demonstrates step-by-step instructions for processing the data and generating the figures featured in the publication.