Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning

Supporting code for the manuscript "Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning", written by: Slavica Dimitrieva, Rens Janssens, Gang Li, Artur Szalata, Raja Gopal, Chintan Parmar, Audrey Kauffmann, Eric Y. Durand. 

MOBER (Multi Origin Batch Effect Remover) is a deep learning-based method that performs biologically relevant integration of transcriptional profiles from pre-clinical models and clinical tumors. MOBER can be used to guide the selection of cell lines and patient-derived xenografts and identify models that more closely resemble clinical tumors. 

Related repository: https://github.com/Novartis/MOBER