Chintanu Sarmah

Microarray technology is expanding rapidly providing an extensive as well as promising source of data for better addressing complex questions involving biological processes. The ever increasing number and publicly available gene expression studies of human and other organisms provide strong motivation to carry out cross-study analyses. Besides, microarray technology provides several platforms to investigators that include arrays from commercial vendors like Affymetrix ® (Santa Clara, CA, USA), Agilent® (Palo Alto, CA, USA), and other proprietorial arrays of various laboratories. Integration of multiple studies that are based on the same technological platform, or, combining data from different array platforms carries the potential towards higher accuracy, consistency and robust information mining. The integrated result often allows constructing a more complete and broader picture. In this work, we highlight as well as exemplify two frameworks of microarray data integration approaches that are in practice. This follows a discussion on the important issues that may influence any microarray data integration attempt. The review, in general, intends to serve as a starting point for those interested in exploring this area of microarray study, while realizing the pertinent issues underneath.

The development of microarray technology for high-throughput measurement of gene expressions is proving a powerful means for studying the transcriptome on a genomic scale and across diverse biological systems and experimental designs. The technology has grown rapidly in academia, medicine and the pharmaceutical , biotechnology, agrochemical and food industries. The technology confers the freedom to conduct experiments in its multiple platforms and attends to the extensive molecular surveillance of cells and tissues. Despite a large number of studies available in the literature comparing results between the existing platforms remains a challenging task in microarray technology. In this direction, we have developed an approach that allows integration of microarray experiments between two of its platforms, i.e. Affy-metrix ® and cDNA microarrays. In this communication , we elaborate on our approach and present our validation. In addition, we have also examined our approach to evaluate where it stands amid a few existing methods.

Microarrays technology has been expanding remarkably since its launch about 15 years ago. With its advancement along with the increase of popularity, the technology affords the luxury that gene expressions can be measured in any of its multiple platforms. However, the generated results from the microarray platforms remain incomparable. In this direction, we earlier developed and tested an approach to address the incomparability of the expression measures of Affymetrix sand cDNA-platforms. The method was an exploit involving transformation of Affymetrix data, which brought the gene expressions of both cDNA and Affymetrix platforms to a common and comparable level. The encouraging outcome of that investigation has subsequently acted as a motivator to focus attention on examining further in the direction of defining the association between the two platforms. Accordingly, this paper takes on a novel exploration towards determining a precise association using a wide range of statistical and machine learning approaches, specifically the various models are elaborately trailed using—regression (linear, cubic-polynomial, LOESS, bootstrap aggregating) and artificial neural networks (self-organizing maps and feedforward networks). After careful comparison, the existing relationship between the data from the two platforms is found to be non-linear where feedforward neural network captures the best delineation of the association.

Abstract—Gene expression profiling is rapidly evolving into a powerful technique for investigating tumor malignancies. The researchers are overwhelmed with the microarray-based platforms and methods that confer them the freedom to conduct large-scale gene expression ...