Automatic registration of microarray images. II. Hexagonal grid
Automatic registration of microarray images. II. Hexagonal grid
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DOI：
10.1093/bioinformatics/btg260
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发表时间：
2003-09-22
期刊：
影响因子：
5.8
通讯作者：
Galinsky, VL
中科院分区：
文献类型：
--
作者：
Galinsky, VL
动机：在本文的第一部分中，作者提出了一种高效、鲁棒和完全自动化的矩形网格微阵列图像点和块索引算法。虽然矩形网格是目前在微阵列载玻片上对探针进行分组的最常见类型，但还有另一种基于光纤束的微阵列技术，其中探针被包装在六边形网格中。与标准矩形填充相比，六边形网格既有优点又有缺点，当然需要调整和/或修改论文第一部分中提出的斑点索引算法。结果：在论文的第二部分中，作者提出了一个版本的斑点索引算法，适用于斑点以六边形结构填充的微阵列图像。该算法是完全自动化的，适用于不同类型的六边形网格，并具有不同的网格间距和旋转参数以及光斑尺寸。它可以成功地跟踪网格的局部和全局变形，包括非正交变换。与第一部分中的算法类似，它与网格大小成线性关系，时间复杂度为O（M），其中M是六边形网格中的网格点总数。该算法已被测试的CCD和扫描图像的斑点表达率低至2%。大约50000个十六进制网格点的图像的处理时间小于一秒。对于高表达率（接近90%）的图像，配准时间甚至更短，大约四分之一秒。
Motivation: In the first part of this paper the author presented an efficient, robust and completely automated algorithm for spot and block indexing in microarray images with rectangular grids. Although the rectangular grid is currently the most common type of grouping the probes on microarray slides, there is another microarray technology based on bundles of optical fibers where the probes are packed in hexagonal grids. The hexagonal grid provides both advantages and drawbacks over the standard rectangular packing and of course requires adaptation and/or modification of the algorithm of spot indexing presented in the first part of the paper.Results: In the second part of the paper the author presents a version of the spot indexing algorithm adapted for microarray images with spots packed in hexagonal structures. The algorithm is completely automated, works with hexagonal grids of different types and with different parameters of grid spacing and rotation as well as spot sizes. It can successfully trace the local and global distortions of the grid, including non-orthogonal transformations. Similar to the algorithm from part 1, it scales linearly with the grid size, the time complexity is O(M), where M is total number of grid points in hexagonal grid. The algorithm has been tested both on CCD and scanned images with spot expression rates as low as 2%. The processing time of an image with about 50000 hex grid points was less than a second. For images with high expression rates (similar to90%) the registration time is even smaller, around a quarter of a second.