ABSTRACT
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. Recent studies have reported that the decomposition methods are the most appropriate for solving these challenges. In this study, we represent an R tool, termed overlapping co-expressed gene module (oCEM), which possesses those methods with a wholly automatic analysis framework to help non-technical users to easily perform complicated statistical analyses and then gain robust results. We also develop a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. Two example datasets are used, related to human breast cancer and mouse metabolic syndrome, to enable the illustration of the straightforward use of the tool. Computational experiment results show that oCEM outperforms state-of-the-art techniques in the ability to additionally detect biologically relevant modules. The R scripts used in the study, including all information on the tool and its usage are made publicly available at https://github.com/huynguyen250896/oCEM.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have corrected some grammatical errors and clarify some points.