短片段寡核苷酸芯片数据的质量评估
Quality assessment for short oligonucleotide microarray data
摘要：
基因表达谱芯片数据的质量是一个新的研究课题。与其他领域一样，微阵列质量通过比较微阵列之间的适当数值总结来评估，以便可以可视化离群值和趋势，并且可以识别质量差的阵列或质量可变的阵列组。由于每个阵列都包含数万或数十万个测量值，因此挑战在于找到可用于进行准确质量调用的数字摘要。为此，引入了几个新的质量措施的基础上探针水平和概率水平的信息，所有获得的低级别的分析算法RMA/fitPLM的AffytechnologyGeneChips的副产品。质量景观空间定位芯片或杂交问题。数值芯片质量措施是从分布的归一化无标度标准误和相对对数表达式。通过残差比例因子评估芯片批次的质量。这些质量评估措施在各种数据集（加标实验，小型实验室实验，多中心研究）上得到证明。将它们与Affyoung的单个芯片质量报告进行比较。
英文摘要：
Quality of microarray gene expression data has emerged as a new research topic. As in other areas, microarray quality is assessed by comparing suitable numerical summaries across microarrays, so that outliers and trends can be visualized, and poor quality arrays or variable quality sets of arrays can be identified. Since each single array comprises tens or hundreds of thousands of measurements, the challenge is to find numerical summaries which can be used to make accurate quality calls. To this end, several new quality measures are introduced based on probe level and probeset level information, all obtained as a by-product of the low-level analysis algorithms RMA/fitPLM for Affymetrix GeneChips. Quality landscapes spatially localize chip or hybridization problems. Numerical chip quality measures are derived from the distributions of Normalized Unscaled Standard Errors and of Relative Log Expressions. Quality of chip batches is assessed by Residual Scale Factors. These quality assessment measures are demonstrated on a variety of datasets (spike-in experiments, small lab experiments, multi-site studies). They are compared with Affymetrix's individual chip quality report.
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