SOJNMF: Identifying Multidimensional Molecular Regulatory Modules by Sparse Orthogonality-Regularized Joint Non-Negative Matrix Factorization Algorithm

Cancer is not only a very aggressive but also a very diverse disease. Recent advances in high-throughput omics technologies of cancer have enabled biomedical researchers to have more opportunities for studying its multi-level biological regulatory mechanism. However, there are few methods to explore...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 19; číslo 6; s. 3695 - 3703
Hlavní autoři: Wang, Yujie, Guan, Tianhao, Zhou, Gang, Zhao, Hongqian, Gao, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cancer
DNA methylation
Factorization
Gene expression
Gene Regulatory Networks - genetics
Gene set enrichment analysis
Germanium
Humans
Joint non-negative matrix factorization (JNMF)
Linear programming
Liver
Liver cancer
Liver Neoplasms - genetics
Matrix converters
Matrix decomposition
Medical research
MicroRNAs - genetics
MicroRNAs - metabolism
miRNA
Modules
Multidimensional methods
multidimensional molecular regulatory modules
multidimensional omics data
Orthogonality
Permutations
Regulatory mechanisms (biology)
Ribonucleic acid
RNA
Sparse matrices
sparse orthogonality-regularized joint non-negative matrix factorization (SOJNMF)
Statistical analysis
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:Cancer is not only a very aggressive but also a very diverse disease. Recent advances in high-throughput omics technologies of cancer have enabled biomedical researchers to have more opportunities for studying its multi-level biological regulatory mechanism. However, there are few methods to explore the underlying mechanism of cancer by identifying its multidimensional molecular regulatory modules from the multidimensional omics data of cancer. In this paper, we propose a sparse orthogonality-regularized joint non-negative matrix factorization (SOJNMF) algorithm which can integratively analyze multidimensional omics data. This method can not only identify multidimensional molecular regulatory modules, but reduce the overlap rate of features among the multidimensional modules while ensuring the sparsity of the coefficient matrix after decomposition. Gene expression data, miRNA expression data and gene methylation data of liver cancer are integratively analyzed based on SOJNMF algorithm. Then, we obtain 238 multidimensional molecular regulatory modules. The results of permutation test indicate that different omics features within these modules are significantly correlated in statistics. Meanwhile, the results of functional enrichment analysis show that these multidimensional modules are significantly related to the underlying mechanism of the occurrence and development of liver cancer.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2021.3114146