Segmentation and intensity estimation for microarray images with saturated pixels

<p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

Melioidosis Vaccines: A Systematic Review and Appraisal of the Potential to Exploit Biodefense Vaccines for Public Health Purposes

The designation of Burkholderia pseudomallei as a category B select agent has resulted in considerable research funding to develop a protective vaccine. This bacterium also causes a naturally occurring disease (melioidosis), an important cause of death in many countries including Thailand and Australia. In this study, we explored whether a vaccine could be used to provide protection from melioidosis. An economic evaluation based on its use in Thailand indicated that a vaccine could be a cost-effective intervention if used in high-risk populations such as diabetics and those with chronic kidney or lung disease. A literature search of vaccine studies in animal models identified the current candidates, but noted that models failed to take account of the common routes of infection in natural melioidosis and major risk factors for infection, primarily diabetes. This review highlights important areas for future research if biodefence-driven vaccines are to play a role in reducing the global incidence of melioidosis