Quantitative quality control in microarray image processing and data acquisition

Abstract

A new integrated image analysis package with quantitative quality control schemes is described for cDNA microarray technology. The package employs an iterative algorithm that utilizes both intensity characteristics and spatial information of the spots on a microarray image for signal-background segmentation and defines five quality scores for each spot to record irregularities in spot intensity, size and background noise levels. A composite score q(com) is defined based on these individual scores to give an overall assessment of spot quality. Using q(com) we demonstrate that the inherent variability in intensity ratio measurements is closely correlated with spot quality, namely spots with higher quality give less variable measurements and vice versa. In addition, gauging data by q(com) can improve data reliability dramatically and efficiently. We further show that the variability in ratio measurements drops exponentially with increasing q(com) and, for the majority of spots at the high quality end, this improvement is mainly due to an improvement in correlation between the two dyes. Based on these studies, we discuss the potential of quantitative quality control for microarray data and the possibility of filtering and normalizing microarray data using a quality metrics-dependent scheme.

A Classification-Based Machine Learning Approach for the Analysis of Genome-Wide Expression Data

“…These are key to the success of individual studies in functional genomics and pharmacogenomics, and are thus essential for rapid discovery. Analyses and datahandling steps to insure data quality have been described and are fairly well characterized for each type of microarray technology (Brown et al 2001;Wang et al 2001). The goals of data analysis steps (after data collection and quality checking) fall into three broad categories: (1) class discovery (identification of subtypes from gene expression profiles), (2) class prediction (placing unknown samples into a preexisting classification), and (3) detecting differentially expressed genes (finding dysregulated genes that clearly delineate between subtypes within a classification).…”

“…Technological fixes and appropriate experimental design can minimize the effects of such artifacts. In fact, labs, facilities, and bioinformatics research centers conducting microarray experiments are making good progress in developing both technological fixes and bioinformatics approaches for quality control, including spot location effects and dye effects (e.g., Brown et al 2001;Wang et al 2001;P. Tonellato, pers.…”

Fluorescent Signal Detection of Immunochromatographic Chip Based on Pyramid Connection and Gaussian Mixture Model

“…At last we do quantitative analysis [10,11] by calculating the average fluorescence intensity in obtained fluorescent areas. It can be proved that the algorithm has a good effect to detect fluorescent signal through experimental verification.…”

Robust Microarray Image Processing

“…The diversity of instrumental platforms and instrumental and biological factors that may influence the result makes formalization difficult and unlikely to be universal. Several attempts have been made to approach the problem (Buhler et al, 2000;Brown et al, 2001;Wang et al, 2001;Chen et al, 2002;Hautaniemi et al, 2003;Bylesjö et al, 2005). Generally a number of parameters characterizing the spot, such as signal-to-noise ratio, size, circularity, etc., are introduced.…”