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

Abstract

BackgroundMissing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers’ time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework.ResultsMVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables.ConclusionsMVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

Large scale in silico characterization of repeat expansion variation in human genomes

“…Prominent examples include Friedreich ataxia (FRDA) caused by a GAA expansion in intron 1 of the FXN gene 6 , amyotrophic lateral sclerosis (ALS) caused by a GGGGCC expansion in intron 1 of the C9ORF72 gene 7 , and myotonic dystrophy type 2 (DM2) caused by a CCTG expansion in intron 1 of the ZFN9 gene 8 . We speculate that there are more repeat expansion loci yet to be discovered because there are over 500,000 TRs mapped in the human genome and these regions are highly mutable 9 .…”

Estimation of missing values in astronomical survey data: An improved local approach using cluster directed neighbor selection

“…Firstly, the knowledge-assisted approach integrates domain knowledge or external information into the imputation process. This has been reported to be useful for a data set with small number of samples or with a high missing level, where both global and local data-driven counterparts would become largely ineffective [19,20]. In cases with sufficient amount of data, the global approach can estimate missing values using a global correlation extracted from the entire data matrix [21].…”

Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique

“…The first is that data obtained from microarray technology often contains missing values (MVs). MVs present a challenge to traditional analysis models that require a complete data matrix [ 5 , 6 ]. Another problem is the high computational complexity caused by data’s high dimensionality [ 7 , 8 ].…”