Abstract
Microarray images in molecular genetics are heavily contaminated by noise and outlying measurements. This paper is devoted to analysis of Illumina BeadChip microarray images, primarily to their low-level preprocessing.
We point out that standard image analysis procedures, which are implemented in the beadarray package of BioConductor software, are highly sensitive to contamination by severe noise and outliers. Therefore, the habitually used methodology does not discover many of the outliers.
We illustrate this on real data and show that the standard background correction method may actually amplify the noise in the image. A robust image analysis tailor-made for this type of microarray images is highly desirable.
We explain principles and show preliminary results of our robust alternative to the standard approach, which aims to be robust to noise and outliers in each its step.