基因表达缺失值的加权回归估计算法

Missing Value Estimation for Microarray Expression Data Based on Weighted Regression

在基因芯片实验中,数据缺失客观存在,并在一定程度上影响芯片数据后续分析结果的准确性。在不增加实验次数的情况下,缺失值估计是降低缺失数据对后续分析影响的有效方法。利用相似性信息的核加权函数来实现缺失值回归估计的局部化,提出了基于加权回归估计的基因表达缺失值估计算法。在两个不同类型的基因芯片数据上,将新方法与几种已知的方法进行了比较分析。实验结果表明,新的估计算法具有比传统缺失值估计算法更好的稳定性和估计准确度。

In mieroarray experiments, the missing value does exist and somewhat affects the stability and precision of the expression data analysis. Compared with increasing experiments, missing value estimating is preferred in reducing the influence of missing values on the post-processing. With the kernel weight based on similarly between target gene and sample genes, which localize missing value estimation, a new method based on weighted regression is presented. On the two real mieroarray expression datasets, the novel method was compared with several existing methods. Experimental results show that the novel method has better stability and precision than the existing methods that have been employed.