Package: EMTscore 0.99.10

EMTscore: Calculate EMT Scores Based on Omics Data

Epithelial-mesenchymal transition (EMT) is an important form of cellular plasticity that is fully or partially activated in several biological scenarios including development and disease progression. EMT involves altered expression of hundreds of protein-coding and non-protein-coding genes. Recent studies showed the prevalence of partial EMT in multiple processes such as various cancers and organ fibrosis, which necessitates rigorous quantification of the degree of EMT. While traditional gene set scoring methods such as gene set variation analysis have been used to generate EMT scores from omics data, multiple EMT scoring algorithms and EMT gene sets have been used by different groups without standardization. Furthermore, comparisons of EMT scores computed from different methods and/or different EMT gene sets are generally difficult due to both the context dependent nature of EMT and the lack of tools that comprehensively integrate varying components for EMT scoring. To address this problem, EMTscore enables users to select scoring methods from a list of previously used algorithms and EMT gene sets from a list of gene sets produced from different experiments. Several visualization methods are provided for making publication-quality plots of EMT scores from omics data. The package also implements a principal-component-analysis based method for scoring divergent EMT processes from a single dataset. Overall, EMTscore provides an integrated solution for assessing the degree and complexity of EMT from omics data, and paves the way for standardizing the comparison of EMT programs across multiple contexts.

Authors:Haimei Wen [aut, cre], Daniel Lopez [aut], Tian Hong [aut], National Institutes of Health [fnd], National Science Foundation [fnd]