SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks

Abstract

Predicting the transcriptomic consequences of cellular perturbations is challenging due to the sparsity of single-cell transcriptional responses, heterogeneous perturbation efficiency, and the difficulty of identifying the small subset of genes that are truly differentially expressed after perturbation. Current approaches typically encode interventions as latent shifts from unperturbed to perturbed cellular state, making the models hard to interpret. Here we introduce SPECTRA (SPEctral CRISPR Transcriptome Regulatory Autoencoder) a graph-based model that treats CRISPR perturbations as interpretable, localized signals injected into a Gene Regulatory Network and propagated through directed graph neural networks. SPECTRA combines a variational control-cell encoder and a directed heterophilic graph decoder, integrating single-cell expression, pretrained transcriptomic context, and prior regulatory topology into a single node-level representation. We evaluate SPECTRA on a large-scale single-cell CRISPRi benchmark, measuring differentially expressed gene recovery through precision, F1 score, and AUPRC-based DEG classification. Since perturbations in SPECTRA are interpretable, we tested the predictions of the model against known biology, in cases where the effects of a gene knock out have been repeatedly demonstrated with orthogonal experiments. Our results show that graph-based signal propagation is a biologically grounded alternative to latent-shift perturbation modeling and can improve the recovery of sparse perturbation-induced transcriptional effects.