Microarray standard data set and figures of merit for comparing data processing methods and experiment designs
Microarray standard data set and figures of merit for comparing data processing methods and experiment designs
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DOI：
10.1093/bioinformatics/btg126
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发表时间：
2003-05-22
期刊：
影响因子：
5.8
通讯作者：
Stoughton, RB
中科院分区：
文献类型：
--
作者：
He, YDD;Dai, HY;Stoughton, RB
动机：在改进微阵列表达谱的平台、实验设计和数据分析方法方面，有一个非常大的和不断增长的努力。随着方法的日益丰富，大多数科学家对于如何在不同的应用中对它们进行客观的比较和选择越来越困惑。有必要为微阵列社区比较和改进分析和统计方法的标准框架。结果：我们报告了一个微阵列数据集，包括204个原位合成的寡核苷酸阵列，每个阵列与来自20种不同人体组织和细胞系的双色cDNA样本杂交。设计了24000个60聚寡核苷酸，这些核苷酸在阵列上报告了2500个类似的已知基因，并设计了杂交实验，以支持替代数据处理方法和替代实验和阵列设计的性能评估。我们还提出了成功检测个体差异表达变化或表达水平的标准价值，以及检测基因和实验中表达模式的相似性和差异性。我们期望该数据集和所提出的优点将为许多微阵列社区提供一个标准框架，以比较和改进与微阵列数据分析相关的许多分析和统计方法，包括图像处理，归一化，误差建模，每个基因的多个报告组合，重复实验的使用以及基于表达变化的测量中的样本参考方案。
Motivation: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods.Results: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the similar to 24 000 60mer oligonucleotides that report similar to2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change.