Empirical study for the agreement between statistical methods in quality assessment and control of microarray data

摘要

As microarray data quality can affect each step of the microarray analysis process, quality assessment and control is an integral part. It detects divergent measurements beyond the acceptable level of random fluctuations. This empirical study identifies association and correlation between the six quality assessment methods for microarray outlier detection used in the arrayQualityMetrics package version 2.2.2. For evaluation two different agreement tests—Cohen’s Kappa, after a homogeneity marginal criteria, and AC1 Statistic—, the Pearson Correlation Coefficient and realistic microarray data from the public ArrayExpress database have been used. It is possible to assess the quality of a data set using only four of the six currently proposed statistical methods to comprehensively quantify the quality information in large series of microarrays. This saves computation time and reduces decision complexity for the analyst. The new proposed rule is validated with data sets from biomedical studies.