Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India.
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Bioinformatics. 2022 Jan 27;38(4):977-984. doi: 10.1093/bioinformatics/btab794.
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms.
In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with 'warp' modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data.
https://github.com/xulabs/aitom.
Supplementary data are available at Bioinformatics online.
冷冻电子断层扫描（cryo-ET）是一种 3D 成像技术，能够在近原子分辨率下对原位亚细胞结构进行可视化。细胞冷冻电子断层扫描图像有助于解析大分子的结构，并确定它们在单个细胞中的空间关系，这在细胞和结构生物学中具有广泛的意义。亚图像分类和识别是系统恢复这些大分子结构的主要步骤。监督深度学习方法已被证明在亚图像分类方面非常准确和高效，但由于注释数据的缺乏，适用性有限。虽然为训练监督模型生成模拟数据是一种潜在的解决方案，但与真实实验数据相比，生成数据中的图像强度分布存在较大差异，这将导致训练后的模型在预测真实亚图像的类别时表现不佳。
在这项工作中，我们提出了 Cryo-Shift，这是一种用于基于深度学习的跨域亚图像分类的完全无监督的域自适应和随机化框架。我们使用无监督的多对抗域自适应来减少模拟和实验数据之间的特征域偏移。我们开发了一种带有“扭曲”模块的网络驱动的域随机化过程，以改变模拟数据并帮助分类器更好地对实验数据进行泛化。我们没有使用任何标记的实验数据来训练我们的模型，而现有的一些替代方法需要标记的实验样本进行跨域分类。然而，Cryo-Shift 在使用模拟和实验数据进行的广泛评估研究中，在跨域亚图像分类方面优于现有的替代方法。
https://github.com/xulabs/aitom。
补充数据可在生物信息学在线获得。
