RT Journal Article
SR Electronic
T1 oCEM: Automatic detection and analysis of overlapping co-expressed gene modules
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.15.435373
DO 10.1101/2021.03.15.435373
A1 Quang-Huy Nguyen
A1 Duc-Hau Le
YR 2022
UL http://biorxiv.org/content/early/2022/01/11/2021.03.15.435373.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsoCEMOverlapping CoExpressed gene ModuleWGCNAweighted gene co-expression network analysisiWGCNAimproved weighted gene co-expression network analysisICAindependent component analysisPCAprincipal component analysisIPCAindependent principal component analysisE.coliEscherichia coliGOGene Ontology