Improving gene quantification by adjustable spot-image restoration

Abstract

Motivation: One of the major factors that complicate the task of microarray image analysis is that microarray images are distorted by various types of noise. In this study a robust framework is proposed, designed to take into account the effect of noise in microarray images in order to assist the demanding task of microarray image analysis. The proposed framework, incorporates in the microarray image processing pipeline a novel combination of spot adjustable image analysis and processing techniques and consists of the following stages: (1) gridding for facilitating spot identification, (2) clustering (unsupervised discrimination between spot and background pixels) applied to spot image for automatic local noise assessment, (3) modeling of local image restoration process for spot image conditioning (adjustable wiener restoration using an empirically determined degradation function), (4) automatic spot segmentation employing seeded-region-growing, (5) intensity extraction and (6) assessment of the reproducibility (real data) and the validity (simulated data) of the extracted gene expression levels. Results: Both simulated and real microarray images were employed in order to assess the performance of the proposed framework against well-established methods implemented in publicly available software packages (Scanalyze and SPOT). Regarding simulated images, the novel combination of techniques, introduced in the proposed framework, rendered the detection of spot areas and the extraction of spot intensities more accurate. Furthermore, on real images the proposed framework proved of better stability across replicates. Results indicate that the proposed framework improves spots' segmentation and, consequently, quantification of gene expression levels. Availability: All algorithms were implemented in Matlab TM (The Mathworks, Inc., Natick, MA, USA) environment. The codes that implement microarray gridding, adaptive spot restoration and segmentation/intensity extraction are available upon request. Supplementary results and the simulated microarray images used in this study are available for download from: ftp://users

Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

“…If this noise is treated improperly, analysis of these images may result in erroneous biological conclusions. Biological noise is intrinsic, it includes the stochastic internal noise of the cell and error sources related to sample preparation, and it induces image blurring [12]. Experimental noise can be subdivided into source noise and detector noise.…”

“…These types of noise produce microarray images, which are corrupted by irregularities in the shape, size, and position of the spots, and are dominated by spatially inhomogeneous noise [12]. The undesirable effect of noise is that, it causes inaccurate spots segmentation, which in turn has a direct effect on the incorrect estimation of the relative mean spots intensities and reduces the reproducibility and validity of the gene expression levels, derived from microarray images.…”

“…Noise complicates all microarray image processing tasks (gridding, segmentation, intensity extraction). For noiseless images, these would have been a trivial task even by using simpler segmentation and feature extraction method, but this is not the case [12].…”

“…These are help only in low-level noise reduction. However, these methods are insufficient, and the presence of noise results in erroneous segmentation and intensity extraction that leads to inaccurate estimation of the log-intensity ratios [12]. To achieve accurate estimation of log-intensity ratios from these images, efficient algorithms for reducing noise is essential for microarray image analysis.…”

Microarray Image Denoising Using a Two-Stage Multiresolution Technique

“…Daskalakis et al [1] introduced a complete framework for microarray image analysis, which takes into account the effect of local spot-image noise in microarray images for improving spot segmentation and subsequently gene quantification.…”