The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data.

细胞中生物分子的分布和动力学在生物学研究中至关重要。拉曼成像技术极大地扩展了我们对细胞生物学系统的了解。导致拉曼仪器最优化的技术发展有助于提高测量速度和灵敏度。除仪器开发外，数据挖掘在揭示光谱数据中包含的复杂化学信息方面也起着重要作用。已经应用了许多数据挖掘方法来提取光谱信息并将其转换为生物学信息。通过将数据挖掘应用于拉曼成像数据，已经实现了单细胞可视化，细胞分类和生物分子/药物定量。在这里，我们总结了拉曼成像数据分析的框架，其中涉及预处理，模式识别和验证。针对分析的每个阶段开发了多种方法。这些方法的特征与它们在细胞的拉曼成像中的应用有关。此外，我们总结了可以促进这些方法实施的软件。通过精心选择和应用，数据挖掘可以作为探索信息丰富的拉曼光谱数据的重要工具。我们总结了可以促进这些方法实施的软件。通过精心选择和应用，数据挖掘可以作为探索信息丰富的拉曼光谱数据的重要工具。我们总结了可以促进这些方法实施的软件。通过精心选择和应用，数据挖掘可以作为探索信息丰富的拉曼光谱数据的重要工具。