Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization
Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization
复制标题
使用多维约束联合非负矩阵分解整合多个基因组成像数据来研究肉瘤肺转移
DOI：
10.1016/j.ins.2021.06.058
复制
发表时间：
2021-06-30
影响因子：
8.1
通讯作者：
Zhang, Hua
中科院分区：
文献类型：
--
作者：
Deng, Jin;Zeng, Weiming;Zhang, Hua
组织病理学图像和基因组数据的综合分析能够发现潜在的生物标志物和多模式关联模式。然而，很少有研究通过将组织病理学图像与多种遗传变异数据相结合，建立针对肉瘤等复杂疾病的有效关联模型。在这里，我们提出了一种称为多维约束联合非负矩阵分解（MDJNMF）的综合多基因组成像框架，用于基于样本匹配的全实体图像、DNA甲基化和拷贝数变异特征来识别与肉瘤肺转移相关的模块。将三种类型的特征矩阵投影到一个公共特征空间上，其中同一投影方向上具有大系数的异质变量形成一个公共模块。利用图像特征和遗传变异特征之间的相关性作为网络正则化约束来提高模块精度。利用稀疏性和正交约束来实现模块化稀疏解。多层次分析表明，我们的方法有效地发现了与肉瘤或肺转移相关的生物学功能模块。代表性模块揭示了图像特征与遗传变异特征之间的显着相关性，并挖掘潜在的诊断生物标志物。总之，所提出的方法为使用其他疾病的多种类型的数据源识别关联模式和生物标志物提供了新的线索。 (c) 2021 Elsevier Inc. 保留所有权利。
Integrative analysis of histopathology images and genomic data enables the discovery of potential biomarkers and multimodal association patterns. However, few studies have established effective association models for complex diseases, such as sarcoma, by combining histopathological images with multiple genetic variation data. Here, we present an integrative multiple genomic imaging framework called multi-dimensional constrained joint non-negative matrix factorization (MDJNMF) to identify modules related to lung metastasis of sarcomas based on sample-matched whole-solid image, DNA methylation, and copy number variation features. Three types of feature matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. The correlation between image features and genetic variation features is used as network-regularized constraints to improve the module accuracy. Sparsity and orthogonal constraints are utilized to achieve the modular sparse solution. Multi-level analysis indicates that our method effectively discovers biologically functional modules associated with sarcoma or lung metastasis. The representative module reveals a significant correlation between image features and genetic variation features and excavates potential diagnostic biomarkers. In summary, the proposed method provides new clues for identifying association patterns and biomarkers using multiple types of data sources for other diseases. (c) 2021 Elsevier Inc. All rights reserved.