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A robust pre-processing of BeadChip microarray images

Kalina J.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 3

556--563

Microarray images commonly used in gene expression studies are heavily contaminated by noise and/or outlying values (outliers). Unfortunately, standard methodology for the analysis of Illumina BeadChip microarray images turns out to be too vulnerable to data contamination by outliers. In this paper, an alternative approach to low-level pre-processing of images obtained by the BeadChip microarray technology is proposed. The novel approach robustifies the standard methodology in a complex way and thus ensures a sufficient robustness (resistance) to outliers. A gene expression data set from a cardiovascular genetic study is analyzed and the performance of the novel robust approach is compared with the standard methodology. The robust approach is able to detect and delete a larger percentage of outliers. More importantly, gene expressions are estimated more precisely. As a consequence, also the performance of a subsequently performed classification task to two groups (patients vs. control persons) is improved over the cardiovascular gene expression data set. A further improvement was obtained when considering weighted gene expression values, where the weights correspond to a robust estimate of variability of the measurements for each individual gene transcript.

Highly robust statistical methods in medical image analysis

Kalina J.

Biocybernetics and Biomedical Engineering

2012

Vol. 32, no. 2

3-16

Standard multivariate statistical methods in medical applications are too sensitive to the assumption of multivariate normality and the presence of outliers in the data. This paper is devoted to robust statistical methods. In the context of medical image analysis they allow to solve the tasks of face detection and face recognition in a database of images. The results of the robust approaches in image analysis turn out to outperform those obtained with standard methods. Robust methods also have desirable properties appealing for practical applications, including dimension reduction and clear interpretability.