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AUPRC:用于评估中扰动方法在识别差异表达基因方面的性能
Hongxu Zhu1, Amir Asiaee2, Leila Azinfar2
1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, 1200 Pressler St., 77030, TX, United States.
Briefings in bioinformatics
|September 1, 2025
概括
计算模型可以预测细胞反应, 我们使用AUPRC的新框架揭示了模型往往无法识别差异表达的基因,从而改善了形评估.
科学领域:
- 计算生物学
- 系统生物学
- 基因组学
背景情况:
- 在中,扰动模型预测细胞对生物变化的反应.
- 目前的评估指标如R平方评估整体准确性,而不是特定的生物结果.
- 识别差异表达 (DE) 基因对于理解细胞反应至关重要.
研究的目的:
- 引入一个新的扰模型评估框架.
- 使用AUPRC指标评估差异表达基因预测的精度和回忆.
- 在单细胞和伪散装数据集上进行基准计算模型.
主要方法:
- 开发了一个新的评估框架,包括精度回忆曲线下的面积 (AUPRC) 度量.
- 应用该框架来评估各种in silico扰动模型.
- 使用单细胞和伪批量实验数据集进行基准测试.
主要成果:
- 在R平方和AUPRC性能指标之间存在显著差异.
- 具有高R平方值的模型通常呈现低AUPRC,表明DE基因识别不佳.
- 新的框架揭示了当前模型预测生物相关基因表达变化的能力的局限性.
结论:
- 传统的指标,如R平方,对于评估波模型来说是不够的.
- AUPRC指标提供了对模型性能更具生物相关性的评估.
- 这种框架提高了计算模型在扰动研究中的可靠性和适用性.
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