Summary-based Comparison of Data Quality across Public MAGE-ML Genomic Datasets

Abstract

Extensive microarray experimental data is available online, facilitating independent evaluation of experiment
conclusions and enabling reuse. Numerous microarray experiment datasets are published using the MAGE-ML
XML schema but assessing the quality of published experiments still represents a challenging task since there is no
consensus among microarray users on a framework to measure datasets quality.
In this paper, we apply techniques based on DescribeX that quantitatively and qualitatively analyze MAGE-ML
public collections, gaining insights about schema evolution. Our case study shows that DescribeX is a useful tool for
the evaluation of microarray experiment data quality that enhances the understanding of the instance-level structure of
MAGE-ML datasets and its evolution.