Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets

A genomic characterization of the influence of silver nanoparticles on bone differentiation in MC3T3-E1 cells

Silver nanoparticles (AgNPs) have been widely used in a variety of biomedical applications. Previous studies demonstrated that AgNPs significantly enhanced bone cell mineralization and differentiation in MC3T3-1 cells, a model in vitro system, when compared to several other NPs. This increased bone deposition was evaluated by phenotypic measurements and assessment of the expression of miRNAs associated with regulation of bone morphogenic proteins. In the present study, we used RNA-seq technology, a more direct measurement of gene expression, to investigate further the mechanisms of bone differentiation induced by AgNP treatment. Key factors associated with the osteoclast pathway were significantly increased in response to AgNP exposure including Bmp4, Bmp6 and Fosl1. In addition, genes of metabolism and toxicity pathways were significantly regulated as well. Although this study suggests the potential for AgNPs to influence bone morphogenesis in injury or disease applications, further investigation into the efficacy and safety of AgNPs in bone regeneration is warranted.

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

BACKGROUND

Microarray 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.

RESULTS

In 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.

CONCLUSIONS

In 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.