MALDI imaging mass spectrometry: statistical data analysis and current computational challenges.
MALDI imaging mass spectrometry: statistical data analysis and current computational challenges.
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DOI：
10.1186/1471-2105-13-s16-s11
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发表时间：
2012
影响因子：
3
通讯作者：
Alexandrov T
中科院分区：
文献类型：
--
作者：
Alexandrov T
基质辅助激光解吸/电离飞行时间（MALDI-TOF）成像质谱法，也称为maldi -成像，是一种用于样品空间分辨化学分析的无标记生物分析技术。通常，maldi成像用于分析特殊制备的组织切片，并将其放置在载玻片上。在过去十年中，已经观察到maldi成像技术的巨大发展。目前，它是生物化学中最有前途的创新测量技术之一，是一种强大而通用的工具，可用于从生物和植物组织到生物和聚合物薄膜的各种样品类型的空间分辨化学分析。本文概述了分析maldi成像数据的计算方法，重点介绍了多元统计方法，讨论了它们的优缺点，并对它们的应用提出了建议。阐述了生物标志物发现的非监督数据挖掘方法和监督分类方法。我们还提出了一个高通量计算管道，用于使用空间分割解释maldi成像数据。最后，我们讨论了当前与maldi成像数据统计分析相关的挑战。
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.