Supplementary Material 3

The Lrn_5000D_Fctrzn_100k_001TH.zip file contains the results of a MOFA factorization of the TGCA learning dataset, to be downloaded and used for transfer learning factorization of a target dataset with MOTL. The MOFA output is in the Model.hdf5 file, and intercepts for the factorization are in the EstimatedIntercepts.rds file. The FctrMeta.json file contains metadata related to the MOFA factorization. The nohup.out file is the log of the factorization.

The expdat_meta.rds file contains metadata from the preprocessing of the TCGA multi-omics learning dataset that was factorized. This should also to be downloaded as it is an input to MOTL

Supplementary Material 4

Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.