Kalinin Alexandr A, Arevalo John, Serrano Erik, Vulliard Loan, Tsang Hillary, Bornholdt Michael, Muñoz Alán F, Sivagurunathan Suganya, Rajwa Bartek, Carpenter Anne E, Way Gregory P, Singh Shantanu
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Nat Commun. 2025 Jun 4;16(1):5181. doi: 10.1038/s41467-025-60306-2.
Large-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.
大规模分析检测通过测量每个细胞或样本的数千种生物学特性来捕捉细胞群体的状态。然而，由于测量的高维度以及非线性、异质性，评估图谱强度和相似性仍然具有挑战性。在这里，我们开发了一个统计框架，使用平均精度均值（mAP）作为单一的数据驱动指标来应对这一挑战。我们通过模拟和实际数据，对照既定指标验证了mAP框架，揭示了其捕捉细胞状态中细微且有意义的生物学差异的能力。具体而言，我们使用mAP来评估样本相对于对照的表型活性，以及扰动（或样本）组的表型一致性。我们在不同的数据集以及不同的图谱类型（图像、蛋白质、mRNA）、扰动（CRISPR、基因过表达、小分子）和分辨率（单细胞、批量）上评估了该框架。mAP框架与我们的开源软件包copairs一起，可用于评估生物学研究和药物发现中的高维分析数据。
