RPA：探测性能的概率分析和稳健的总结
RPA: Probabilistic analysis of probe performance and robust summarization
摘要：
探针级模型已经导致微阵列研究中性能的提高，但是探针级污染的各种来源仍然知之甚少。探头性能的数据驱动分析可用于量化单个探头的不确定性，并突出不同噪声源的相对贡献。提高对探针水平效应的理解可以改进预处理技术和微阵列设计。我们已经实现了探针性能分析和总结短寡核苷酸阵列的概率工具。与标准的预处理方法相比，该方法提供了探针特异性噪声和亲和力项的定量估计以及用于调查这些参数的工具。还提供了将探针的先验信息纳入分析的工具。与已知的探针级误差源和峰值数据集的比较验证了该方法。可在R/BioConductor中免费获得实现：http://www.bioconductor.org/packages/release/bioc/html/RPA.html
英文摘要：
Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood. Data-driven analysis of probe performance can be used to quantify the uncertainty in individual probes and to highlight the relative contribution of different noise sources. Improved understanding of the probe-level effects can lead to improved preprocessing techniques and microarray design. We have implemented probabilistic tools for probe performance analysis and summarization on short oligonucleotide arrays. In contrast to standard preprocessing approaches, the methods provide quantitative estimates of probe-specific noise and affinity terms and tools to investigate these parameters. Tools to incorporate prior information of the probes in the analysis are provided as well. Comparisons to known probe-level error sources and spike-in data sets validate the approach. Implementation is freely available in R/BioConductor: http://www.bioconductor.org/packages/release/bioc/html/RPA.html
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