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用UniFORM进行多重组织成像的通用免疫光规范化

Kunlun Wang¹, Kaoutar Ait-Ahmad¹, Sam Kupp¹

¹Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, Oregon Health & Science University (OHSU), Portland, OR, USA.

Cell reports methods

|September 9, 2025

概括

UniFORM 是一个新的 Python 管道,它规范化了多重组织成像数据. 它有效地减少了技术变异,保留了生物信号,以便进行更准确的分析.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
图像分析图像分析

背景情况:

多重组织成像 (MTI) 生成复杂的数据集,需要强大的规范化.
现有的MTI规范化方法难以平衡批量效应去除与生物信号保存.

研究的目的:

引入UniFORM,一个用于MTI数据规范化的新型非参数Python管道.
在MTI数据中解决特征和像素级别的技术变化.
提高MTI分析的准确性和生物解释性.

主要方法:

UniFORM使用自动化的刚性地标注册方法,适合MTI数据特征.
管道运行没有先前的分配假设,容纳单模和双模模式.
它调整负数种群以消除技术变异,同时保持正数种群表达模式.

主要成果:

与现有方法相比,UniFORM在三个MTI平台上表现出卓越的性能.
观察到改善的标志物分布对齐和积极的种群保存.
增强的k-最近邻方批量效应测试 (kBET) 和轮得分表明数据质量更好.

结论:

UniFORM有效地减轻了MTI数据中的批量效应,同时保持了生物信号的真实性.
该管道支持更连贯的下游分析,包括UMAP和Leiden集群.
UniFORM的可扩展设计和可选的微调模式为MTI数据规范化提供了广泛的适用性.

关键词:

CP:计算生物学计算机生物学 CP: 图像处理图像处理批量纠正批量纠正多重组织成像多重组织成像规范化的正常化.

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相关概念视频

Immunofluorescence Microscopy

Immunofluorescence Microscopy

A fluorescence microscope uses fluorescent chromophores called fluorochromes, which can absorb energy from a light source and then emit this energy as visible light. Fluorochromes include naturally fluorescent substances (such as chlorophylls) and fluorescent stains that are added to the specimen to create contrast. Dyes such as Texas red and FITC are examples of fluorochromes. Other examples include the nucleic acid dyes 4’,6’-diamidino-2-phenylindole (DAPI), and acridine orange.