oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Abstract

Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

DrGA: cancer driver gene analysis in a simpler manner

“…Also, although iWGCNA better performs its original version in terms of identifying biologically relevant functional modules, we understand that there has an absolute difference between clustering patients into different subgroups and clustering genes into different modules. Therefore, we have raised this point and proposed a novel tool named oCEM to overcome it, published elsewhere [ 35 ]. In the future, we will consider replacing iWGCNA with oCEM.…”

“…Basically, WGCNA attempts to build co-expressed modules of genes based on a gene-gene similarity matrix across a group of patients having a tendency to show a coordinated expression pattern [22]. Our previous study [16] introduced an improved version of WGCNA, temporarily called iWGCNA in this study, and confirmedly outperformed its original version in the ability to detect functional gene modules [35]. Specifically, we predetermined which cluster distance measure, including the single-linkage, complete-linkage, average-linkage, or Ward's minimum variance [36] methods (Table 1 and Fig.…”

Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas

“…oCEM ( Nguyen and Le, 2022 ) was very recently developed by us to serve for the same task as the two tools above. The tool first detected principal components using either the independent component analysis (ICA) ( Comon, 1994 ; Hyvärinen and Oja, 2000 ; Liebermeister, 2002 ) or the independent principal component analysis (IPCA) ( Yao et al, 2012 ).…”

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

“…How to mine biological knowledge from these data effectively is an urgent problem to be solved. Many studies show that genes do not function individually, so the gene module is considered an useful tool for interpreting gene expression profiles [1][2][3]. Generally, a module is a group of coexpressed genes (genes with similar expressions) with similar functions [3,4], which is mainly obtained through Gene Co-expression Network (GCN) analysis [5] currently, so it can also be called a co-expressed module.…”

“…a gene can belong to one or more modules) are more reflective of the real biological system [14,15]. Based on independent component analysis (ICA), principal component analysis (PCA), and independent principal component analysis (IPCA), Nguyen et al proposed a method for the identification of overlapping gene co-expression modules: oCEM, which had good performance in detecting clinically relevant co-expression modules [3]. Camila et al further extended WGCNA, using hierarchical link clustering to identify communities (modules) in co-expression networks, and identified 19 rice genes associated with salt stress [15].…”