About the Project
Application of deep learning to genomic studies is an exciting area that is rapidly evolving and holds the potential to revolutionize genomics. Our group employs deep learning techniques to tackle several problems related to single-cell RNA-seq data analysis. We have developed methods for
- Prediction of circadian phase (Auerbach et al. 2022, Nature Communications)
- Single-cell clustering and batch effect removal (Li et al. 2020, Nature Communications)
- Transfer learning for single-cell clustering (Hu et al. 2020, Nature Machine Intelligence)
- Joint gene expression imputation, clustering and batch effect removal (Lakkis et al. 2021, Genome Research)
- Single-cell RNA-seq and CITE-seq integration (Lakkis et al. 2022, Nature Machine Intelligence)
