Testing the Transferability of Perturbational Similarity Maps Across Species, Cell Systems, and Assay Modalities.

IF 2.9 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jing Wen
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引用次数: 0

Abstract

We evaluated a mouse whole-brain perturbation map in human K562 and RPE1 Perturb-seq, HeLa morphology and nine L1000 cell-line matrices using three endpoints: global pair-rank concordance, source-defined top-k neighbour preservation, and within-validation CORUM retrieval. Strict orthology retained 71, 90, 695 and 273-311 shared targets, respectively. Under the primary zero-anchored RMS representation, Spearman rho was 0.026 and 0.035 in K562 and RPE1, 0.0022 and 0.0055 in the two HeLa conditions, and a median of 0.0055 across L1000 lines. RPE1 depended on representation: across-target standardisation gave rho = 0.102 (95% CI, 0.027-0.201), whereas omitting feature RMS scaling gave a descriptive rho = -0.034. No source-defined top-10 neighbour preservation result survived correction across 13 contexts (minimum q = 0.240). Source pair ranks also changed when response features were restricted. Within-validation CORUM retrieval identified complex structure in several external matrices but did not test preservation of source neighbours. Across 50 cell splits, K562/RPE1 median global rho was 0.600/0.710 and top-10 overlap was 0.497/0.517. Across 50 mouse-level splits of K562/RPE1-matched source profiles, median global rho was 0.731/0.703; a cross-species noise ceiling remains unestimated. The results are conditional on representation and feature selection and do not establish absence of transfer or transferability to neural systems.

测试跨物种,细胞系统和分析模式的微扰相似性图的可转移性。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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