Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering
Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering
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DOI：
10.1093/bioinformatics/btr246
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发表时间：
2011-07-01
期刊：
影响因子：
5.8
通讯作者：
Kobarg, Jan Hendrik
中科院分区：
文献类型：
--
作者：
Alexandrov, Theodore;Kobarg, Jan Hendrik
动机：成像质谱（IMS）是少数几种生物化学测量技术之一，它在给定薄样品的情况下，能够在整个分子范围内揭示其空间化学组成。IMS产生高光谱图像，其中对于每个像素测量高维质谱。目前，该技术已经足够成熟，阻碍其推广的主要问题之一是挖掘大型IMS数据集的计算方法还不发达。本文提出了一种新的IMS数据集的空间分割方法，它是考虑到像素到像素的变异性的重要问题而构建的。重要的是，我们将像素之间的空间关系纳入聚类，使像素与它们的邻居聚集在一起。我们提出了两种方法。一种是非自适应的，其中像素邻域对于所有像素以相同的方式选择。第二个方面是数据中可观察到的结构。对于一个像素，它的邻域是考虑到它的光谱与相邻像素的光谱的相似性来定义的。这两种方法具有线性的复杂性，并需要线性的存储空间（在光谱的数量）。结果：所提出的分割方法进行评估的两个IMS数据集：一个大鼠脑切片和一个神经内分泌肿瘤的一部分。他们发现解剖结构，区分肿瘤区域并突出功能相似的区域。此外，如果与其他最先进的方法相比，我们的方法提供了类似或更好质量的分割图，但在运行时和/或所需内存中优于它们。
Motivation: Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability.Methods: We segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra).Results: The proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory.