{"scope":{"layer":"general","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"preprint","importance":"","importance_reason":""},"id":"VC0180","type":"preprint","title":"Transcriptional perturbation effects remain structured beyond the leading global trend","authors_or_org":"Liu Q, Zhang Q, Chen Y, Du J-H, Zhao S, Wang J.","date":"2026-10-11","year":"2026","venue":"bioRxiv (preprint, not peer-reviewed)","url":"https://doi.org/10.64898/2026.10.05.756839","doi":"10.64898/2026.10.05.756839","pmid":"","brain_tumor_focus":"no","tags":["perturbation","benchmark","critique"],"tag_slugs":["perturbation","benchmark","critique"],"tag_labels":["Perturbation","Benchmark & validation","Critique"],"tag_labels_zh":["扰动","基准评测与验证","批评与反证"],"unmapped_tags":[],"one_line_summary_en":"Preprint (not peer-reviewed): Liu et al. (Department of Statistics, University of Chicago) revisit the observation that many gene perturbations move cells along a common global transcriptional direction. They report that perturbations contribute differently to this leading trend and that substantial structure remains beyond it, including perturbation-specific effects that transfer across datasets and a common hierarchy of responsive genes; these patterns make simple prediction strategies surprisingly competitive even beyond the leading global component.","one_line_summary_zh":"预印本（未经同行评审）：Liu 等（芝加哥大学统计系）重新审视“许多基因扰动使细胞沿同一全局转录方向移动”的现象。作者报告：不同扰动对这一主导趋势的贡献不同，趋势之外仍有大量结构，包括可跨数据集迁移的扰动特异效应和跨扰动共有的响应基因层级；这些规律使简单预测策略即便在主导全局成分之外也颇具竞争力。","verified":"yes","verification_source":"bioRxiv API details (title, 6 authors, posted 2026-10-11, v1, new results, genomics, CC BY-NC-ND, University of Chicago, abstract) fetched 2026-10-20; full text not read","page_url":"https://gliostate.com/library/VC0180/","api_url":"https://gliostate.com/api/items/VC0180.json","sameAs":["https://doi.org/10.64898/2026.10.05.756839"],"is_preprint":true,"facets":{"type":"https://gliostate.com/library/type/preprint/","year":"https://gliostate.com/library/year/2026/","tags":["https://gliostate.com/library/tag/perturbation/","https://gliostate.com/library/tag/benchmark/","https://gliostate.com/library/tag/critique/"]},"cited_by":[],"related":[{"id":"VC0179","title":"A split-control criterion for few-shot adaptation in single-cell perturbation prediction","shared_tags":["perturbation","benchmark","critique"],"page_url":"https://gliostate.com/library/VC0179/","api_url":"https://gliostate.com/api/items/VC0179.json"},{"id":"VC0145","title":"A comparison of computational methods for expression forecasting.","shared_tags":["benchmark","critique","perturbation"],"page_url":"https://gliostate.com/library/VC0145/","api_url":"https://gliostate.com/api/items/VC0145.json"},{"id":"VC0144","title":"Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.","shared_tags":["benchmark","critique","perturbation"],"page_url":"https://gliostate.com/library/VC0144/","api_url":"https://gliostate.com/api/items/VC0144.json"},{"id":"VC0146","title":"Deep learning perturbation models can outperform baselines on calibrated metrics.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0146/","api_url":"https://gliostate.com/api/items/VC0146.json"},{"id":"VC0048","title":"Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0048/","api_url":"https://gliostate.com/api/items/VC0048.json"},{"id":"VC0049","title":"Benchmarking foundation cell models for post-perturbation RNA-seq prediction.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0049/","api_url":"https://gliostate.com/api/items/VC0049.json"}]}