SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms

The field of mass spectrometry imaging is currently devoid of standardized protocols or commercially available products designed for system suitability testing of MSI platforms. Machine learning is an approach that can quickly and effectively identify complex patterns in data and use them to make informed classifications, but there is a technical barrier to implementing these algorithms. Here we package the machine learning algorithms into a user-friendly interface to make community-wide implementation of this protocol possible. The software package is built entirely in the Python language using the PySimpleGUI library for the construction of the interface, Pandas and Numpy libraries for data formatting and manipulation, and the Scikit-Learn library for the implementation of machine learning algorithms. Training data is collected on a clean and compromised instrument that can then be used to evaluate model performance and to train models prior to interrogating unknown samples before, du..., , , # SLICE-MSI Executable and Example Data [https://doi.org/10.5061/dryad.msbcc2g7c](https://doi.org/10.5061/dryad.msbcc2g7c) ## Description of the data and file structure The collected data comes from a novel QC mix detected on a clean and compromised IR-MALDESI-MSI platform. The corresponding software package is a graphical user interface that incorporates machine learning algorithms for efficient and effective classification of instrument condition. This work was completed to fill a current void in the MSI community and provide an easy-to-use and easily implementable quality control and system suitability testing protocol for MSI. ### Files and variables #### File: QC\_Testing.csv **Description:** CSV containing one replicate from the complete dataset to act as a testing set to be used alongside the user manual. Any missing values present are due to the lack of detection of the analyte in that scan. For example, if the analyte is not detected in the ROI the abundance cell will be ...质谱成像（mass spectrometry imaging, MSI）领域目前尚无针对MSI平台系统适用性测试的标准化协议或商用产品。机器学习可快速高效地识别数据中的复杂模式，并据此做出可靠分类，但落地此类算法仍存在技术壁垒。本研究将机器学习算法封装为用户友好型界面，使该方案得以在全MSI社区推广应用。 该软件包完全基于Python语言开发：界面构建使用PySimpleGUI库，数据格式化与处理依托Pandas与NumPy库，机器学习算法的实现则借助Scikit-Learn库。训练数据采集自正常与受损的质谱仪，可用于评估模型性能，并在分析未知样品前对模型进行训练。before, du..., , , # SLICE-MSI可执行文件与示例数据集 https://doi.org/10.5061/dryad.msbcc2g7c ## 数据与文件结构说明 本数据集采集自基于正常与受损IR-MALDESI-MSI平台检测的新型质控混合物。配套软件包为一款图形用户界面，集成了机器学习算法，可高效准确地分类仪器运行状态。本研究旨在填补MSI领域当前的空白，为MSI提供一款易用且易部署的质量控制与系统适用性测试方案。 ### 文件与变量 #### 文件：QC_Testing.csv **描述：** 该CSV文件包含完整数据集的一组重复样本，用作测试集，可配合用户手册使用。文件中存在的缺失值源于对应扫描中未检测到分析物。例如，若在感兴趣区域（region of interest, ROI）内未检测到分析物，则其丰度单元格将……