mxnorm: An R Package to Normalize Multiplexed Imaging Data.
mxnorm: An R Package to Normalize Multiplexed Imaging Data.
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DOI：
10.21105/joss.04180
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发表时间：
2022-01-01
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通讯作者：
Vandekar, Simon
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文献类型：
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作者：
Harris, Coleman;Wrobel, Julia;Vandekar, Simon
多重成像是一种新兴的单细胞检测方法，可用于了解和分析基于组织的癌症、自身免疫性疾病等的复杂过程。这些成像技术，包括索引共检测（CODEX）、多路离子束成像（MIBI）和多路免疫荧光成像（MxIF），提供了关于细胞之间空间相互作用的详细信息（Angelo et al. 2014; Gerdes等人，2013; Goltsev等人，2018年）。多路复用成像实验在数百个载玻片和图像中生成数据，通常会产生TB级的复杂数据，需要通过成像分析管道进行分析。方法正在快速发展以改进流水线的特定部分，包括R和Python中的软件包，如spatialTime、imcRtools、MCMICR 0和Squidpy（Creed等人，2021; Palla等人，2021; Schapiro等人，2021; Windhager等人，2021年）。该管道的一个重要但未得到充分研究的组件是分析这个复杂数据源中的技术变化-强度归一化是消除这种技术变化的一种方法。不同的预处理管道、成像变量、光学效果和载玻片内依赖性的组合创建了可以通过归一化方法减少的批次和载玻片效果。目前最先进的方法在研究实验室和图像采集平台之间差异很大，没有一种单一的方法是一致的鲁棒性-最佳统计方法试图通过消除这种技术差异来提高图像和载玻片之间的相似性，同时保持数据中的潜在生物信号。mxnorm是一款开源软件，采用R和S3方法构建，可实现、评估和可视化多路成像数据的归一化技术。扩展Harris等人（2022）中描述的方法，我们打算为R.这使得用户可以轻松地将标准化方法扩展到该领域，并提供了一个强大的评估框架来衡量各种标准化方法的技术可变性和有效性。R包的一个关键组成部分是能够提供用户定义的归一化方法和阈值算法，以评估多路复用成像数据的归一化。核心特性、使用细节和广泛的教程可以在CRAN和软件存储库的软件包文档和小插图中找到。
Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source - intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust - optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.