Konishi Tomokazu
Faculty of Bioresource Sciences, Akita Prefectural University, Shimo-Shinjyo, Akita 010-0195, Japan.
Genome Inform. 2006;17(2):100-9.
Gene expression microarray data often include problems caused by uneven hybridization and dust contamination. Such problems should be removed prior to analysis to prevent degradation of analytical accuracy and false positive results. This paper presents a parameter-scanning algorithm to detect such defects on the basis of the character of data distributions. The cell data is thoroughly scanned using a window algorithm, and windows with an index value greater than a threshold are recognized as defects and removed from the array data. The index is found from the differences between the target and an ideal standard of hybridization obtained as a trimmed mean among experiments, representing the statistical center of differences in each section. The threshold is derived as a screening level designated by the operator, but has only limited effect on the effectiveness of data cancellation. The validity of the algorithm and the effects of data cancellation are tested using GeneChip data obtained from a series of experiments. The algorithm is demonstrated to greatly improve the reproducibility of measurements, and removes only a small number of faultless data.
基因表达微阵列数据常常存在由杂交不均和灰尘污染导致的问题。在分析之前应消除此类问题，以防止分析准确性下降和出现假阳性结果。本文提出一种基于数据分布特征来检测此类缺陷的参数扫描算法。使用窗口算法对细胞数据进行全面扫描，将索引值大于阈值的窗口识别为缺陷并从阵列数据中移除。该索引是通过目标值与作为实验中截尾均值获得的理想杂交标准之间的差异得出的，代表每个部分差异的统计中心。阈值是由操作人员指定的筛选水平，但对数据消除的有效性影响有限。使用从一系列实验中获得的基因芯片数据来测试该算法的有效性和数据消除的效果。结果表明该算法极大地提高了测量的可重复性，并且只移除了少量无缺陷的数据。
