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用于对通过组织微阵列获得的免疫组织化学染色数据进行高通量分析和存档的软件工具。

Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays.

作者信息

Liu Chih Long, Prapong Wijan, Natkunam Yasodha, Alizadeh Ash, Montgomery Kelli, Gilks C Blake, van de Rijn Matt

机构信息

Department of Biochemistry, Stanford University Medical Center, California 94305, USA.

出版信息

Am J Pathol. 2002 Nov;161(5):1557-65. doi: 10.1016/S0002-9440(10)64434-3.

Abstract

The creation of tissue microarrays (TMAs) allows for the rapid immunohistochemical analysis of thousands of tissue samples, with numerous different antibodies per sample. This technical development has created a need for tools to aid in the analysis and archival storage of the large amounts of data generated. We have developed a comprehensive system for high-throughput analysis and storage of TMA immunostaining data, using a combination of commercially available systems and novel software applications developed in our laboratory specifically for this purpose. Staining results are recorded directly into an Excel worksheet and are reformatted by a novel program (TMA-Deconvoluter) into a format suitable for hierarchical clustering analysis or other statistical analysis. Hierarchical clustering analysis is a powerful means of assessing relatedness within groups of tumors, based on their immunostaining with a panel of antibodies. Other analyses, such as generation of survival curves, construction of Cox regression models, or assessment of intra- or interobserver variation, can also be done readily on the reformatted data. Finally, the immunoprofile of a specific case can be rapidly retrieved from the archives and reviewed through the use of Stainfinder, a novel web-based program that creates a direct link between the clustered data and a digital image database. An on-line demonstration of this system is available at http://genome-www.stanford.edu/TMA/explore.shtml.

摘要

组织微阵列(TMA)的创建使得能够对数千个组织样本进行快速免疫组织化学分析,每个样本可使用多种不同抗体。这项技术发展催生了对辅助分析和存档存储所产生的大量数据的工具的需求。我们开发了一个用于TMA免疫染色数据高通量分析和存储的综合系统,该系统结合了市售系统以及我们实验室专门为此目的开发的新型软件应用程序。染色结果直接记录到Excel工作表中,并通过一个新程序(TMA解卷积程序)重新格式化,以适合进行层次聚类分析或其他统计分析的格式呈现。层次聚类分析是基于肿瘤组对一组抗体的免疫染色来评估肿瘤组内相关性的有力手段。其他分析,如生存曲线的生成、Cox回归模型的构建或观察者内或观察者间变异的评估,也可以很容易地在重新格式化的数据上进行。最后,可以通过使用Stainfinder(一个新型的基于网络的程序,它在聚类数据和数字图像数据库之间建立直接链接)从存档中快速检索特定病例的免疫图谱并进行查看。该系统的在线演示可在http://genome-www.stanford.edu/TMA/explore.shtml上获取。

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