Fluctuation structure predicts genome-wide perturbation outcomes.

ABSTRACT: Pooled single-cell perturbation screens represent powerful experimental platforms for functional genomics, yet interpreting these rich datasets for meaningful biological conclusions remains challenging. Most current methods fall at one of two extremes: either opaque deep learning models that obscure biological meaning, or simplified frameworks that treat genes as isolated units. As such, these approaches overlook a crucial insight: gene co-fluctuations in unperturbed cellular states can be harnessed to model perturbation responses. Here we present CIPHER (Covariance Inference for Perturbation and High-dimensional Expression Response), a conceptual framework leveraging linear response theory from statistical physics to predict transcriptome-wide perturbation outcomes using gene co-fluctuati

SUBMITTER: Kuznets-Speck B 

PROVIDER: S-EPMC12236818 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature