On estimation in real-time microarrays

Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady-state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed in (Hassibi, 2007). The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In the current paper, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross-hybridization

Modeling and Estimation for Real-Time Microarrays

Microarrays are used for collecting information about a large number of different genomic particles simultaneously. Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes (e.g., DNA fragments) bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In this paper, we study the signal processing aspects of the real-time microarray system design. In particular, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross hybridization. These are important steps toward developing optimal detection algorithms for real-time microarrays, and to understanding their fundamental limitations

Signal Processing Aspects of Real-Time DNA Microarrays

Data acquisition in conventional fluorescent-based microarrays takes place after the completion of a hybridization phase. During the hybridization phase, target analytes bind to their corresponding capturing probes on the array. The conventional microarrays attempt to detect presence and quantify amounts of the targets by collecting a single data point, supposedly taken after the hybridization process has reached its steady-state. Recently, so-called real-time microarrays capable of acquiring not only the steady-state data but the entire kinetics of hybridization have been proposed in [1]. The richness of the information obtained by the real-time microarrays promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we study the signal processing aspects of the real-time microarray data acquisition

Signal Processing for Real-Time DNA Microarrays

In conventional fluorescent-based microarrays, data is acquired after the completion of the hybridization phase. In this phase the target analytes (i.e., DNA fragments) bind to the capturing probes on the array and supposedly reach a steady state. Accordingly, microarray experiments essentially provide only a single, steady-state data point of the hybridization process. On the other hand, a novel technique (i.e., real-time microarrays) capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed in [1]. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we model the kinetics of the hybridization process measured by the realtime microarrays, and develop techniques for estimating the amounts of analytes present therein

Real-time DNA microarray analysis

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays

Modeling the kinetics of hybridization in microarrays

Conventional fluorescent-based microarrays acquire data after the hybridization phase. In this phase the targets analytes (i.e., DNA fragments) bind to the capturing probes on the array and supposedly reach a steady state. Accordingly, microarray experiments essentially provide only a single, steady-state data point of the hybridization process. On the other hand, a novel technique (i.e., realtime microarrays) capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed in [5]. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we develop a probabilistic model of the kinetics of hybridization and describe a procedure for the estimation of its parameters which include the binding rate and target concentration. This probabilistic model is an important step towards developing optimal detection algorithms for the microarrays which measure the kinetics of hybridization, and to understanding their fundamental limitations

Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm