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000301772 1001_ $$00000-0003-4563-3226$$aKalinin, Alexandr A$$b0
000301772 245__ $$aA versatile information retrieval framework for evaluating profile strength and similarity.
000301772 260__ $$a[London]$$bSpringer Nature$$c2025
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000301772 520__ $$aLarge-scale profiling assays capture a cell population's state by measuring thousands of biological properties per cell or sample. However, evaluating profile strength and similarity remains challenging due to the high dimensionality and non-linear, heterogeneous nature of measurements. Here, we develop a statistical framework using mean average precision (mAP) as a single, data-driven metric to address this challenge. We validate the mAP framework against established metrics through simulations and real-world data, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we use mAP to assess a sample's phenotypic activity relative to controls, as well as the phenotypic consistency of groups of perturbations (or samples). We evaluate the framework across diverse datasets and on different profile types (image, protein, mRNA), perturbations (CRISPR, gene overexpression, small molecules), and resolutions (single-cell, bulk). The mAP framework, together with our open-source software package copairs, is useful for evaluating high-dimensional profiling data in biological research and drug discovery.
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000301772 650_2 $$2MeSH$$aSoftware
000301772 650_2 $$2MeSH$$aHumans
000301772 650_2 $$2MeSH$$aGene Expression Profiling: methods
000301772 650_2 $$2MeSH$$aInformation Storage and Retrieval: methods
000301772 650_2 $$2MeSH$$aComputational Biology: methods
000301772 650_2 $$2MeSH$$aPhenotype
000301772 7001_ $$00000-0002-1138-5036$$aArevalo, John$$b1
000301772 7001_ $$aSerrano, Erik$$b2
000301772 7001_ $$0P:(DE-He78)0c2194ad1f41bacabc3124d343a91476$$aVulliard, Loan$$b3$$udkfz
000301772 7001_ $$aTsang, Hillary$$b4
000301772 7001_ $$aBornholdt, Michael$$b5
000301772 7001_ $$aMuñoz, Alán F$$b6
000301772 7001_ $$00000-0002-9778-5400$$aSivagurunathan, Suganya$$b7
000301772 7001_ $$aRajwa, Bartek$$b8
000301772 7001_ $$00000-0003-1555-8261$$aCarpenter, Anne E$$b9
000301772 7001_ $$00000-0002-0503-9348$$aWay, Gregory P$$b10
000301772 7001_ $$00000-0003-3150-3025$$aSingh, Shantanu$$b11
000301772 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-025-60306-2$$gVol. 16, no. 1, p. 5181$$n1$$p5181$$tNature Communications$$v16$$x2041-1723$$y2025
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