Transcriptomic similarity guides source selection beyond tissue lineage
Perturbational and pharmacogenomic maps remain incomplete across cellular contexts, leaving a practical source-selection question before response prediction: which measured context should inform an unmeasured target? We tested whether baseline transcriptomic similarity ranks candidate sources for held-out targets, separating source ranking from aggregation and transfer-uncertainty decisions. Similarity tracked matched-response concordance in LINCS and GSE306429; selected-source utility exceeded random expectation in GSE306429 (ΔU = 0.034; P = 0.002) and in exact-time-matched MIX-Seq tasks (ΔU = 0.046). In the full GDSC2 analysis, the drug-balanced ranking effect was 0.359 (95% CI, 0.337-0.383). In a lineage-restricted analysis of the same eligible subset, within-tissue ranking remained positive at 0.130 (95% CI, 0.119-0.141), retaining 35.8% of the same-subset global effect (0.363; 95% CI, 0.342-0.386). Global transcriptomic top-1 selection exceeded tissue-matched random selection by 0.154 (95% CI, 0.143-0.164). Thus, baseline similarity adds source-ranking information after tissue matching in the evaluated setting, while ranking alone does not resolve whether to aggregate sources or how to quantify transfer uncertainty.
Authors
- Haojun Zhang (ORCID: https://orcid.org/0000-0002-5993-5260)
- Xiai Wu (ORCID: https://orcid.org/0009-0006-3662-1978)
- Jiahong Pang
- Li Jiang
- Bote Luo
- Xin Li
Institutions
- Chinese Academy of Sciences (CN)
- China-Japan Friendship Hospital (CN)
- Academy of Mathematics and Systems Science (CN)
- Shaanxi University of Chinese Medicine (CN)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-10-29
- DOI
- https://doi.org/10.64898/2026.10.26.761823
- Primary Topic
- Computational Drug Discovery Methods
- Type
- preprint
Funders
- National Natural Science Foundation of China