Smart Machine and Pattern Recognition Laboratory - MIRP, Faculty of Engineering, Instituto Tecnologico Metropolitano ITM, Medellin, 050012, Colombia.
Biomedical Innovation and Research Group, Faculty of Applied and Exact Sciences, Instituto Tecnologico Metropolitano ITM, Medellin, 050034, Colombia.
BMC Bioinformatics. 2020 Aug 31;21(1):376. doi: 10.1186/s12859-020-03713-0.
Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images.
An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%.
The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.
二维凝胶电泳（2-DGE）是一种常用于蛋白质组学分析的工具。这种基于凝胶的技术根据样品中的等电点和分子量分离蛋白质。2-DGE 图像由于采集过程常常会出现异常，例如：弥散和重叠斑点以及背景噪声。本研究提出了一种联合预处理框架，该框架结合了非线性滤波、背景校正和图像归一化技术的功能，用于预处理 2-DGE 图像。最重要的是，使用合成数据和真实的 2-DGE 图像评估了联合非线性扩散滤波、自适应分段直方图均衡化和多级阈值处理。
与实施归一化、背景校正或滤波单一技术相比，使用所提出的框架，合成数据的斑点检测效率提高了高达 46%。此外，与归一化技术相比，该框架提高了合成数据中低丰度斑点的检测率 20%，与背景校正技术相比，提高了背景估计值 67%。在真实数据方面，联合预处理框架将假阳性减少了高达 93%。
所提出的联合预处理框架优于单一方法的结果。最佳结构是自适应分段直方图均衡化用于图像归一化、几何非线性扩散（GNDF）用于滤波和多级阈值用于背景校正的有序组合。
