Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering
Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering
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DOI：
10.1021/pr100734z
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发表时间：
2010-12-01
影响因子：
4.4
通讯作者：
Maass, Peter
中科院分区：
文献类型：
--
作者：
Alexandrov, Theodore;Becker, Michael;Maass, Peter
近年来，基质辅助激光解吸/电离（MALDI）-成像质谱已成为一种成熟的技术，允许可重复的高分辨率测量来定位蛋白质和更小的分子。尽管有这种令人印象深刻的技术进步，但只有少数论文发表了关于MALDI成像数据的计算方法。我们解决了这个问题，提出了一种新的MALDI空间分割方法，成像数据集该过程基于它们的相似性将所有光谱聚类到不同的组中该划分由分割图表示，这有助于理解样本的空间结构我们的分割过程的核心是对应于特定质量的图像的边缘保留去噪，其减少像素到像素的可变性并显著改善分割图此外，在应用去噪之前，我们减少了数据集，选择出现在至少1%我们使用所提出的流水线分析了两个数据集，首先对于大鼠脑冠状截面，计算的分割图突出了脑的解剖和功能结构，其次解释了侵入小肠的神经内分泌肿瘤的截面。其中区分肿瘤区域并指示功能相似的区域
In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules However, despite this impressive technological advance only a few papers have been published concerned with computational methods for MALDI-imaging data We address-this issue-proposing a new procedure for-spatial segmentation of MALDI-imaging data sets This procedure clusters all spectra into different groups based on their similarity This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra High dimensional discriminant clustering completes the procedure We analyzed two data sets using the proposed pipeline First for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain Second a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated