Reconstructing Background of DNA Microarray Imagery

DNA微阵列技术使生物学家能够研究整个生物体中的所有基因，以获得基因相互作用和调控的全局视图。然而，该技术仍处于发展初期，并且在微阵列过程的每个主要阶段（点样、杂交和扫描）都可能引入错误。因此，所收集的微阵列图像数据通常包含误差和噪声，这些误差和噪声随后将向下传播到所有后续处理和分析阶段。因此，为了实现这种技术的潜力，获得高质量的图像数据是至关重要的，这些数据确实反映了样本中的潜在生物学。如果不能做到这一点，许多微妙和低水平的基因表达基因，这往往是生物学意义，将无法分析。虽然最近有很多关于如何检测和消除这些变化和错误的研究，但进展缓慢。在过去的两年中，我们已经开始研究开发一种新的方法来处理微阵列图像数据，通过重建背景噪声的微阵列芯片，这在提取高质量的cDNA图像数据已经显示出许多早期的承诺。我们没有使用校正信号异常的标准方法，而是专注于尽可能准确地估计噪声，以至于我们几乎忽略了信号，直到处理的最后阶段。拟议的项目汇集了来自图像处理，数据挖掘和分子生物学不同领域的专业知识，以推动这一重要领域的最新技术水平的跨学科尝试。这是特别及时的，因为迫切需要有既能节省时间和劳力又能提供高质量图像数据的图像分析软件。

DNA microarray technology has enabled biologists to study all the genes within an entire organism to obtain a global view of gene interaction and regulation. However, the technology is still early in its development, and errors may be introduced at each of the main stages of the microarray process: spotting, hybridisation, and scanning. Consequently the microarray image data collected often contain errors and noise, which will then be propagated down through all later stages of processing and analysis. Therefore to realise the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. If this is not achieved many of the subtle and low level gene expression genes, which are often of biological significance, will not be analysed. Although there is recently much research on how to detect and eliminate these variations and errors, the progress has been slow. Over the last two years, we have initiated research to develop a novel way of processing microarray image data by reconstructing background noise of the microarray chip, and this has shown much early promise in extracting high quality cDNA image data. Instead of using the standard approach of correcting anomalies in the signal, we focus on estimating the noise as accurately as possible, to the extent that we almost ignore the signal until the last stage of processing. The proposed project brings together expertise from the disparate fields of image processing, data mining and molecular biology to make an interdisciplinary attempt in advancing the state of art in this important area. It is particularly timely since there is an urgent need to have image analysis software that can save both time and labour as well as provide high-quality image data.