Original authors: Tongxin Wang, Travis S Johnson, Wei Shao, Zixiao Lu, Bryan R Helm, Jie Zhang and Kun Huang Original code and data for using BERMUDA, a novel transfer-learning-based method for batch-effect correction in single cell RNA sequencing (scRNA-seq) data.
Using BERMUDA to define a latent space capturing tissue-specific variation in diffusion MRI parameters, corrected for inter-subject variation (cf. batch).
- Python 3.7.6
- scikit-learn 0.22.1
- pyTorch 1.4.0
- rpy2 2.9.4
- R libraries: simstudy, clusterGeneration, Matrix
main_synthetic.py: example using synthetic data
change data generation to vary number of voxels in each tissueimprove plotsclassifier in latent space, project to discriminative axes?- plots for proba classifications
- implement homogeneity/divergence scores for train/test data?
Wang, T., Johnson, T.S., Shao, W. et al. BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes. Genome Biol 20, 165 (2019) doi:10.1186/s13059-019-1764-6