Wu Wei-Sheng, Jhou Meng-Jhun
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.
BMC Bioinformatics. 2017 Jan 13;18(1):31. doi: 10.1186/s12859-016-1429-3.
Missing 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.
MVIAeval 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.
MVIAeval 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.
缺失值插补对于微阵列数据分析很重要，因为存在缺失值的微阵列数据会显著降低下游分析的性能。尽管已经开发了许多微阵列缺失值插补算法，但仍缺乏一个客观、全面的性能比较框架。为了解决这个问题，我们之前提出了一个框架，该框架可以对不同的现有算法进行全面的性能比较。新算法的性能也可以通过我们的性能比较框架进行评估。然而，对于感兴趣的研究人员来说，构建我们的框架并非易事。为了节省研究人员的时间和精力，我们在此展示一个名为MVIAeval（缺失值插补算法评估器）的易于使用的网络工具，它实现了我们的性能比较框架。
MVIAeval提供了一个用户友好的界面，允许用户上传其新算法的R代码，并选择：（i）20个基准微阵列（时间序列和非时间序列）数据集中的测试数据集；（ii）12种现有算法中的比较算法；（iii）三种现有性能指标中的性能指标；（iv）两种可能选择中的综合性能得分；（v）模拟运行次数。然后生成综合性能比较结果，并以图表形式显示。
MVIAeval是一个有用的工具，研究人员可以使用它轻松地对新开发的微阵列数据或任何可以表示为矩阵形式的数据（例如NGS数据或蛋白质组学数据）的缺失值插补算法进行全面、客观的性能评估。因此，MVIAeval将极大地加快缺失值插补算法的研究进展。
