Brás L P, Menezes J C
Centre for Chemical and Biological Engineering, Department of Chemical and Biological Engineering, IST, Technical University of Lisbon, Portugal.
Syst Biol (Stevenage). 2006 May;153(3):105-19. doi: 10.1049/ip-syb:20050056.
Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.
本文介绍了对不同方法进行比较评估，以估计微阵列数据中的缺失值：加权K近邻插补法（KNNimpute）、基于回归的方法，如局部最小二乘插补法（LLSimpute）和偏最小二乘插补法（PLSimpute）以及贝叶斯主成分分析（BPCA）。阐明了一些因素对预测准确性的影响，如方法参数、估计过程中使用的数据关系类型（即按行、按列或两者皆用）、缺失率和模式以及实验类型[时间序列（TS）、非时间序列（NTS）或混合（MIX）实验]。提出了基于数据迭代使用的改进方法（迭代LLS和PLS插补——ILLSimpute和IPLSimpute）、执行初始插补的必要性（改进的PLS和Helland PLS插补——MPLSimpute和HPLSimpute）以及所采用的关系类型（KNNarray、LLSarray、HPLSarray和交替PLS——APLSimpute）。总体而言，结果表明数据集属性（实验类型、缺失率和模式）会影响数据相似性结构，从而影响方法的性能。在具有更强相似性结构的数据（TS和MIX实验）中，LLSimpute和ILLSimpute更可取，而在估计NTS缺失数据时，基于PLS的方法（MPLSimpute、IPLSimpute和APLSimpute）更可取。
