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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.11.532240v1?rss=1
Authors: Liu, Y.
Abstract: Many computational tools have been developed for high-throughput omics data, and some are very popular, such as limma, WGCNA, and EnrichR. However, they also exhibit disadvantages in some special cases, such as imbalanced data analysis, causal inference, gene network functional enrichment, etc. Hence, we developed the R package eOmics to provide a comprehensive pipeline with these problems addressed. It combines an ensemble framework with limma, improving its performance on imbalanced data. Moreover, it couples a mediation model with WGCNA, so the causal relationship among WGCNA modules, module features, and phenotypes can be found, and this model is also used to explore the relationship between different omics. In addition, our package has some novel functional enrichment methods, capturing the influence of topological structure on gene set functions. Finally, it contains multi-omics clustering and classification functions to facilitate machine-learning tasks. Some basic functions, such as ANOVA analysis, are also available in it. The effectiveness of our package is proved by its performance on the three single or multi-omics datasets here. eOmics is available at: https://github.com/yuabrahamliu/eOmics.
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