EMTscore infers divergent EMT pathways from omics data and enables rapid screening for correlated gene sets

Abstract Summary Quantitative analyses of epithelial-mesenchymal transition (EMT) have been widely used in several areas of biomedical sciences due to its importance in development and cancer progression, but its multi-contextual nature requires standardization and implementation of gene set scoring methods beyond capacities of conventional tools. We developed EMTscore, a package that provides an efficient implementation of unbiased scoring methods for multiple EMT pathways using individual single-cell or bulk omics data, and the package allows rapid screening for relationships between EMT and other cellular processes. Availability and Implementation EMTscore is available from GitHub https://github.com/wenmm/EMTscore under the GNU General Public License, and it will be deposited to Zenodo upon acceptance. It is also under review at Bioconductor. Contact Tian Hong ( hong@utdallas.edu )