{"doi":"10.1101/2025.06.27.661814","title":"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-fluctuations in unperturbed cells. We validated CIPHER on synthetic regulatory networks before applying it to 11 large-scale single-cell perturbation datasets covering 4,234 perturbations and over 1.36M cells. CIPHER robustly recapitulated genome-wide responses to single and double perturbations by exploiting baseline gene covariance structure. Importantly, eliminating gene-gene covariances, while retaining gene-intrinsic variances, reduced model performance by 11-fold, demonstrating the rich information stored within baseline fluctuation structures. Moreover, gene-gene correlations transferred successfully across independent experiments of the same cell type, revealing stereotypic fluctuation structures. Furthermore, CIPHER outperformed conventional differential expression metrics in identifying true perturbations while providing uncertainty-aware effect size estimates through Bayesian inference. Finally, most genome-wide responses propagated through the covariance matrix along approximately three independent and global gene modules. CIPHER underscores the importance of theoretically-grounded models in capturing complex biological responses, highlighting fundamental design principles encoded in cellular fluctuation patterns.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":558376,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9537,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1458810,"name":"Leon Schwartz","orcid":"0000-0001-8621-4146","position":1,"is_corresponding":false},{"id":1458811,"name":"Hanxiao Sun","orcid":"0000-0003-3347-3253","position":2,"is_corresponding":false},{"id":1176922,"name":"Madeline E. Melzer","orcid":"0009-0000-2607-6490","position":3,"is_corresponding":false},{"id":1458812,"name":"Nitu Kumari","orcid":"0000-0002-6348-997X","position":4,"is_corresponding":false},{"id":624284,"name":"Benjamin Haley","orcid":"0000-0002-0074-0020","position":5,"is_corresponding":false},{"id":1458813,"name":"Ekta Prashnani","orcid":"0009-0000-5086-2416","position":6,"is_corresponding":false},{"id":933129,"name":"Suriyanarayanan Vaikuntanathan","orcid":"0000-0003-2431-6045","position":7,"is_corresponding":false},{"id":281300,"name":"Yogesh Goyal","orcid":"0000-0003-3502-6465","position":8,"is_corresponding":false},{"id":1426635,"name":"Benjamin Kuznets-Speck","orcid":"0000-0001-9859-8749","position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:55:25.969263Z","pmid":"40631127","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}