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Improving Microarray Expressions with Recalibration

机译:用重新校准改进微阵列表达式

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Due to the nature of microarray experiments, gene expression levels across and through slide channels can experience up to 103 fold change differences in intensity. Such data variance is caused by `noise' elements, which can influence final expressions. This paper proposes a simple technique whereby histogram transformations are used to reduce noise artefacts. Akin to a magic eraser (removing the top layer of a surface), the technique attempts to blend pixels associated with gene spots into their background. The identification of pixels is relatively straightforward, but blending them with appropriate values is non-trivial. Once replacement values are determined, the background should be a good approximation of the original. By subtracting this surface from the original, gene spot regions would be more accurate. Experiments were carried out and results compared to “GenePix” a mainstream microarray process and “O'Neill” a microarray specific reconstruction algorithm. Not only was our process shown to be significantly quicker in execution time, it also reduced final expression results while typically generating less variation within gene's.
机译:由于微阵列实验的性质,基因表达水平横跨和通过滑动通道可以经历高达103倍的强度变化差异。这种数据方差是由“噪声”元素引起的,这可以影响最终表达式。本文提出了一种简单的技术,从而使用直方图转换来减少噪声伪影。类似于魔法橡皮擦(去除表面的顶层),该技术试图将与基因斑点相关联的像素混合到其背景中。像素的识别相对简单,但是以适当的值混合它们是非微小的。确定更换值后,背景应该是原始的良好近似。通过从原版减去这种表面,基因斑点区域将更加准确。进行实验和结果与“Genepix”是主流微阵列过程和“o'neill”一种微阵列特异性重建算法。不仅我们在执行时间内显示出明显更快的过程,它还减少了最终表达结果,同时通常在基因内产生较少的变化。

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