A simple Affymetrix ratio-transformation method yields comparable expression level quantifications with cDNA data

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 profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data

Microarray data integration: frameworks and a list of underlying issues

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

Microarray gene expression: a study of between-platform association of Affymetrix and cDNA arrays

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®- and 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 in the end, the existing relationship between the data from the two platforms is found to be nonlinear where feedforward neural network captures the best delineation of the association

Microarray gene expression: towards integration and between-platform association of affymetrix and cDNA arrays

Microarrays technology reveals an unprecedented view into the biology of DNA. Information science is moulding this revolution in gene expression profiling with its distinctive skilfulness to transform it into a technologically-advanced and perpetually rejuvenating branch of science while simultaneously contributing to further streamlining the processes involved. With the advancement of the technology along with the increase of popularity, microarrays afford the luxury that gene expressions can be measured in any of its multiple platforms, which include arrays from commercial vendors like Affymetrix (Santa Clara, CA, USA), Agilent (Palo Alto, CA, USA), and other proprietorial arrays of various laboratories. The 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. 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. Various comparison studies have been published over the years, and the overall observation on accuracy, reliability and reproducibility of microarray investigations can be summarized as cautious optimism. In the midst of all the relentless chase in finding suitable remedies for the issues of microarray data integration, this project is an attempt of cross-platform data integration belonging to chilhood leukaemia patients tested on microarray platforms, Affymetrix and cDNA. Keeping in mind the nature of the resultant microarray data from the two platforms, a new ratio-transformation method has been proposed, and is applied to the cancer data. The approach, subsequently, highlights that its usage can address the issue of incomparability of the expression measures of Affymetrix and cDNA platforms. The method is, later, tested against two established approaches, and is found to produce comparative results. The encouraging cross-platform outcome leads to focus attention on examining further in the direction of defining the association between the two platforms. With this motivation, a wide range of statistical as well as machine learning approaches is applied to the microarray data. Specifically, the modelling of the data is elaborately explored using – regression models (linear, cubic-polynomial, loess, bootstrap aggregating) and artificial neural networks (self-organizing maps and feedforward networks). In the end, the existing relationship between the data from the two platforms is found to be nonlinear, which can be well-delineated by feedforward network with relatively more precision than the rest of the methods tested

A simple Affymetrix ratio-transformation method yields comparable expression level quantifications with cDNA data

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 profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data

A simple Affymetrix ratio-transformation method yields comparable expression level quantifications with cDNA data

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 profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data