Purpose: Microarray experiments are important tools for high throughput gene quantification. Nevertheless, such experiments are confounded by a
number of technical factors, which operate at the
fabrication, target labelling, and hybridization
stages, and result in spatially inhomogeneous noise.
Unless these sources of error are addressed, they will
propagate throughout the stages of the analysis,
leading to inaccurate biological inferences. The aim
of this study was to investigate whether image
restoration techniques may improve the accuracy of
subsequent microarray image analysis steps (i.e.
segmentation and gene quantification).
Materials and Methods: A public dataset of seven
microarrays obtained from the MicroArray Genome
Imaging & Clustering Tool (MAGIC) database were
used. Each image contained 6400 spots investigating
the diauxic shift of Saccharomyces cerevisiae.
Restoration was based on the Wiener deconvolution.
Subsequently, restored images were processed with
the MAGIC tool for semi-automatic griding and
segmentation. The influence of the restoration
process on the accuracy of spot segmentation was
quantitatively assessed by the information theoretic
metric of the Kullback-Liebler divergence.
Results: Pre-processing based on Wiener
deconvolution increased the range of divergence
(0.04 – 3.01 bits) and consequently improved the
accuracy of subsequent spot segmentation.
Conclusion: Information theoretic metrics confirmed
the importance of image restoration as a preprocessing
step that significantly improved the
accuracy of subsequent segmentation, thus leading to
more accurate gene quantification.
(EN)
