Liu Chih Long, Montgomery Kelli D, Natkunam Yasodha, West Robert B, Nielsen Torsten O, Cheang Maggie C U, Turbin Dmitry A, Marinelli Robert J, van de Rijn Matt, Higgins John P T
Biological and Biomedical Sciences Program, Division of Medical Sciences, Harvard Medical School, Cambridge, MA 02138, USA.
Mod Pathol. 2005 Dec;18(12):1641-8. doi: 10.1038/modpathol.3800491.
We have previously published a suite of software tools that facilitates the reformulation of tissue microarray (TMA) data so that it may be analyzed using techniques originally devised for analysis of cDNA microarray data. However, current microarray data often feature multiple scores for a given tissue sample and antibody combination. Furthermore, an efficient and systematic method for combining scores that takes into account the differing staining properties of tissue epitopes has not been described. We thus present the TMA-Combiner, a new Microsoft Excel-based macro that permits analysis of data for which tissues may have two or more scores per antibody, and permits combination of data from multiple different tissue microarrays. It accomplishes this by rendering one score per tissue per antibody from two or more scores, using one of multiple user-selectable combination rules developed to account for the differing staining properties of tissue epitopes. This greatly facilitates analysis of tissue microarrays, particularly for users with large repositories of data, and may facilitate discovery of biological trends and help refine diagnostic accuracy of tissue markers in clinical samples.
我们之前发表了一套软件工具,可促进组织微阵列(TMA)数据的重新格式化,以便能够使用最初设计用于分析cDNA微阵列数据的技术对其进行分析。然而,当前的微阵列数据对于给定的组织样本和抗体组合通常具有多个分数。此外,尚未描述一种考虑组织表位不同染色特性的有效且系统的分数组合方法。因此,我们展示了TMA组合器,这是一种基于Microsoft Excel的新宏,它允许分析每个抗体每个组织可能有两个或更多分数的数据,并允许组合来自多个不同组织微阵列的数据。它通过使用为考虑组织表位不同染色特性而开发的多个用户可选择的组合规则之一,从两个或更多分数中为每个抗体每个组织得出一个分数来实现这一点。这极大地促进了组织微阵列的分析,特别是对于拥有大量数据存储库的用户,并且可能有助于发现生物学趋势并提高临床样本中组织标记物诊断准确性。