Wan Tong, Fu Changping, Peng Jiinbo, Lu Jinling, Li Pengcheng, Zhuo JunJie
State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.
Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China.
Biomed Opt Express. 2024 May 1;15(6):3541-3554. doi: 10.1364/BOE.522078. eCollection 2024 Jun 1.
The hippocampus is a critical brain region. Transcriptome data provides valuable insights into the structure and function of the hippocampus at the gene level. However, transcriptome data is often incomplete. To address this issue, we use the convolutional neural network model to repair the missing voxels in the hippocampus region, based on Allen institute coronal slices in situ hybridization (ISH) dataset. Moreover, we analyze the gene expression correlation between coronal and sagittal dataset in the hippocampus region. The results demonstrated that the trend of gene expression correlation between the coronal and sagittal datasets remained consistent following the repair of missing data in the coronal ISH dataset. In the last, we use repaired ISH dataset to identify novel genes specific to hippocampal subregions. Our findings demonstrate the accuracy and effectiveness of using deep learning method to repair ISH missing data. After being repaired, ISH has the potential to improve our comprehension of the hippocampus's structure and function.
海马体是一个关键的脑区。转录组数据为从基因层面了解海马体的结构和功能提供了有价值的见解。然而，转录组数据往往不完整。为了解决这个问题，我们基于艾伦脑科学研究所的冠状切片原位杂交（ISH）数据集，使用卷积神经网络模型修复海马体区域中缺失的体素。此外，我们分析了海马体区域冠状和矢状数据集之间的基因表达相关性。结果表明，在修复冠状ISH数据集中的缺失数据后，冠状和矢状数据集之间的基因表达相关趋势保持一致。最后，我们使用修复后的ISH数据集来识别海马体亚区域特有的新基因。我们的研究结果证明了使用深度学习方法修复ISH缺失数据的准确性和有效性。修复后，ISH有潜力提高我们对海马体结构和功能的理解。
