Correlating measurements across samples improves accuracy of large-scale expression profile experiments
Correlating measurements across samples improves accuracy of large-scale expression profile experiments
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DOI：
10.1186/gb-2009-10-12-r143
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发表时间：
2009-01-01
期刊：
影响因子：
12.3
通讯作者：
Califano, Andrea
中科院分区：
文献类型：
--
作者：
Alvarez, Mariano Javier;Sumazin, Pavel;Califano, Andrea
基因表达谱分析技术在重复实验中的再现性差。然而，当分析大型数据集时，探针水平表达谱相关性可以帮助识别有缺陷的探针，并导致构建具有改善的再现性的更真实的探针集。我们描述的方法，以消除无信息和有缺陷的探针，占探针之间的依赖性，并解决由于转录异构体混合物的变异性。我们测试和验证了我们的方法Affyellow微阵列，并概述了他们未来的适应其他技术。
Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies.