Robust Processing of Microarray Data by Independent Component Analysis

Microarray Data Processing is becoming a field of important activity for Signal Processing and Pattern Recognition areas, as the extraction and mining of meaningful data from large groupings of microarray patterns is of vital importance in Medicine, Genomics, Proteomics, Pharmacology, etc. In this paper emphasis is placed on studying and cataloging the nature of possible sources of corruption of microarray data and in establishing a pre-processing methodology for discriminating sources of corruption from microarray data (denoising). We also discuss ways of precisely reconstructing original contributions (theoretically hybridized data) using ICA methods. Some classical examples are shown, and a discussion follows the presentation of results.