Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

Abstract

BackgroundGene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures × time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set.ResultsWe found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost.ConclusionOur findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

“…However, if the signal of this time section is missing, prediction will be less accurate. In the study for future values, prediction such as air pollution, gene expression, and traffic data, missing values are recovered through observed data, interpolation [21], support vector regression (SVR) [22], Bayesian-based PCA [23], neural networks, an autoregressive integrated moving average (ARIMA), and regression model [24] besides the methods that are used in our experiment.…”

Missing value imputation methods for gene-sample-time microarray data analysis

“…Least square optimization is used to find the coefficients of the linear combination, which are then used for estimating the missing values of the incomplete gene. Other methods based on least squares regression are also introduced in [6,7,8]. The Bayesian principal component analysis method, BPCAimpute, applies PCA similarly to the SVD method.…”

Computer Image Feature Extraction Algorithm for Robot Visual Equipment

“…The LBP and its variations have already been successfully applied for various texture recognition, face recognition, visual speech recognition, etc. [6][7][8][9][10][11]. The [12] combination of SIFT and the rotation invariant LBP has also been reported, *Address correspondence to this author at the College of Computer and Information Engineering, Tianjin Chengjian University, Tianjin, 300384, China; Tel: 13821773575; Fax: 02588574154; E-mail: kkkwangli@163.com which increased the robustness of the illumination invariance, with greater vector dimension of the rotation invariant LBP, leading to increase in the calculated amount.…”