Yu Zeng, Li Tianrui, Horng Shi-Jinn, Pan Yi, Wang Hongjun, Jing Yunge
IEEE Trans Nanobioscience. 2017 Jan;16(1):21-33. doi: 10.1109/TNB.2016.2636243. Epub 2016 Dec 6.
Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes. Then, an accelerating strategy is added to the LAW-LSimpute method in order to improve the convergence. Furthermore, an iterative missing value estimation framework of LAW-LSimpute (ILAW-LSimpute) is designed. Experimental results show that the ILAW-LSimpute method is able to reduce the estimation error.
微阵列数据常常包含缺失值，这会显著影响后续分析。现有的基于最小二乘插补法（LLSimpute）处理缺失数据的方法已被证明总体上是有效的。然而，所有基于LLSimpute的方法在缺失值估计过程中都没有考虑目标基因不同邻居的不同重要性，而是平等对待所有邻居。本文提出了一种局部自动加权最小二乘插补（LAW-LSimpute）方法用于缺失值估计，该方法可以根据基因的重要性自动对相邻基因进行加权。然后，在LAW-LSimpute方法中添加了一种加速策略以提高收敛性。此外，还设计了LAW-LSimpute的迭代缺失值估计框架（ILAW-LSimpute）。实验结果表明，ILAW-LSimpute方法能够降低估计误差。
