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, we have built a toolbox named 'EMTscore' that 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. We provided several visualization methods for making publication quality plots of 'EMT' scores from 'omics' data. Furthermore, we showed a unique utility of a method based on principal component analysis 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 it paves the way for standardizing the comparison of EMT programs across multiple contexts.