  Motivation: Microarray results accumulated in public repositories are widely
re-used in meta-analytical studies and secondary databases. The quality of the
data obtained with this technology varies from experiment to experiment and
efficient method for quality assessment is neces-sary to ensure their
reliability. Results: The lack of a good benchmark has hampered evaluation of
existing methods for quality control. In this study we propose a new
inde-pendent quality metric that is based on evolutionary conservation of
expression profiles. We show, using 11 large organ-specific datasets, that
IQRray, a new quality metrics developed by us, exhibits the highest correlation
with this reference metric, among 14 metrics tested. IQRray outperforms other
methods in identification of poor quality arrays in dataset composed of arrays
from many independent experiments. In con-trast, the performance of methods
designed for detecting outliers in a single experiment like NUSE and RLE was
low because of the inability of these method to detect datasets containing only
low quality arrays, and the fact that the scores cannot be directly compared
between ex-periments. Availability: The R implementation of IQRray is available
at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R
