Missing value imputation for microarray data: a comprehensive comparison study and a web tool

Abstract

BackgroundMicroarray data are usually peppered with missing values due to various reasons. However, most of the downstream analyses for microarray data require complete datasets. Therefore, accurate algorithms for missing value estimation are needed for improving the performance of microarray data analyses. Although many algorithms have been developed, there are many debates on the selection of the optimal algorithm. The studies about the performance comparison of different algorithms are still incomprehensive, especially in the number of benchmark datasets used, the number of algorithms compared, the rounds of simulation conducted, and the performance measures used.ResultsIn this paper, we performed a comprehensive comparison by using (I) thirteen datasets, (II) nine algorithms, (III) 110 independent runs of simulation, and (IV) three types of measures to evaluate the performance of each imputation algorithm fairly. First, the effects of different types of microarray datasets on the performance of each imputation algorithm were evaluated. Second, we discussed whether the datasets from different species have different impact on the performance of different algorithms. To assess the performance of each algorithm fairly, all evaluations were performed using three types of measures. Our results indicate that the performance of an imputation algorithm mainly depends on the type of a dataset but not on the species where the samples come from. In addition to the statistical measure, two other measures with biological meanings are useful to reflect the impact of missing value imputation on the downstream data analyses. Our study suggests that local-least-squares-based methods are good choices to handle missing values for most of the microarray datasets.ConclusionsIn this work, we carried out a comprehensive comparison of the algorithms for microarray missing value imputation. Based on such a comprehensive comparison, researchers could choose the optimal algorithm for their datasets easily. Moreover, new imputation algorithms could be compared with the existing algorithms using this comparison strategy as a standard protocol. In addition, to assist researchers in dealing with missing values easily, we built a web-based and easy-to-use imputation tool, MissVIA (http://cosbi.ee.ncku.edu.tw/MissVIA), which supports many imputation algorithms. Once users upload a real microarray dataset and choose the imputation algorithms, MissVIA will determine the optimal algorithm for the users' data through a series of simulations, and then the imputed results can be downloaded for the downstream data analyses.

Optimal Recovery of Missing Values for Non-negative Matrix Factorization

“…They use the Average Distance Between Partition (ADBP) to calculate clustering error, and they show that BPCA, LLS, and kNN give similar clustering results. Chiu et al find that LLS-like algorithms performed better than kNN-like algorithms in terms of downstream clustering accuracy (measured using cluster pair proportions) [31]. De Souto et al evaluate whether the effect of different imputation methods on clustering and classification are statistically significant [32].…”

Improved k-nearest neighbors approach for incomplete and contaminated gene expression datasets

“…A variety of reasons involve for missing values in GE data such as corruption of image, scratches on the slides, poor hybridization, inadequate resolution, fabrication errors and so on (Schuchhardt et al 2000, Tuikkala et al 2006. Microarray GE datasets typically contain 1-10% missing values that could affect up to 90% of genes (Chiu et al 2013). On the other hand, outliers may also occur in GE datasets due to different steps of data generating process from hybridization to image analysis for various reasons (Shahjaman et al 2017b).…”

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

“…Filling missing values with zeros or with the row average is a simple imputation strategy, but it is far from optimal. Therefore, many advanced algorithms have been developed to impute the missing values in microarray data [10–12]. The existing algorithms can be divided into four categories [11]: global approach, local approach, hybrid approach and knowledge-assisted approach.…”

“…To meet the need, we previously developed a performance comparison framework [12] which provides 13 testing microarray datasets, three types of performance indices, 9 existing algorithms, and 110 runs of simulation. We found that no single algorithm can perform best for all types of microarray data.…”

“…We found that no single algorithm can perform best for all types of microarray data. The best algorithms are different for different microarray data types (time series and non-time series) and different performance indices, showing the usefulness of our framework for conducting a comprehensive performance comparison [12]. …”