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

Nguyen, Q.-H.; Le, D.-H.

doi:10.1101/2021.03.15.435373

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bioRxiv · 10.1101/2021.03.15.435373

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

Nguyen, Q.-H.·Le, D.-H.

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Abstract

BackgroundWhen 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. ResultsThis 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. ConclusionsoCEM 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.

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Nguyen, Q.-H., Le, D.-H.. 2021-03-16. overlappingCGM: Automatic detection and analysis of overlapping co-expressed gene modules. https://doi.org/10.1101/2021.03.15.435373

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