Network-based cancer genomic data integration for pattern discovery

Fangfang Zhu,Jiang Li,Juan Liu,Wenwen Min

doi:10.1186/s12863-021-01004-y

Abstract

BackgroundSince genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.ResultsIn this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered.ConclusionsAll the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

Highlights

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data
Simulation study We evaluated the performance of Sparse Network-regularized SVD (SNSVD) on the simulated data by comparing it with other sparse Singular value decomposition (SVD) based methods including L0SVD [15], ALSVD [4] and SCADSVD [4, 25]
We found that the performance of our proposed method (SNSVD) is superior to that of other methods

Summary

Introduction

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. This model can only handle the gene expression data where no gene interaction information is integrated. Several Sparse Singular Value Decomposition (SSVD) based methods have been proposed for biclustering gene expression data to discover gene functional modules (biclusters) [14], such as ALSVD [4], L0SVD [15], and so on. Most of them ignore the prior gene interaction network knowledge from a protein protein interaction (PPI) network, whereas such information is very useful to improve biological interpretability of discovered gene modules [16,17,18].

Results

Discussion

Conclusion