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Robust segmentation and analysis of DNA microarray spots using an adaptative split and merge algorithm.

机译:使用自适应拆分和合并算法对DNA微阵列斑点进行稳健的分割和分析。

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摘要

Microarray images push to their limits classical analysis methods, since gene spots are often poorly contrasted, ill defined and of irregular shapes. These characteristics hinder a robust quantification of corresponding values for red and green intensities as well as their R/G ratio. New approaches are thus needed to ensure accurate data extraction from these images. Herein we present an automatic non-supervised algorithm for a fast and accurate spot data extraction from DNA microarrays. The method is based on a split and merge algorithm, relying on a Delaunay triangulation process, allowing an incremental partition of the image into homogeneous polygons. Geometric properties of triangles as well as homogeneity criteria are defined according to the specificities of microarray image signals. The method is first assessed on simulated data, and then compared with GenePix and Jaguar Softwares. Results in segmentation and quantification are superior to those obtained from a number of standard techniques forspot extraction.
机译:微阵列图像将经典分析方法推向了极限,因为基因斑点通常对比度差,定义不清且形状不规则。这些特性阻碍了对红色和绿色强度及其R / G比的相应值进行可靠的量化。因此需要新的方法来确保从这些图像中准确提取数据。本文中,我们提出了一种自动非监督算法,用于从DNA微阵列中快速,准确地提取点数据。该方法基于拆分和合并算法,依赖于Delaunay三角剖分过程,从而可以将图像增量划分为同构多边形。根据微阵列图像信号的特异性来定义三角形的几何特性以及均一性标准。首先根据模拟数据评估该方法,然后将其与GenePix和Jaguar软件进行比较。分割和定量分析的结果优于从许多用于斑点提取的标准技术获得的结果。

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