- Computational methods for single-cell omics across modalities
- Single-cell multimodal omics: the power of many
- Undisclosed, unmet and neglected challenges in multi-omics studies
- Computational methods for the integrative analysis of single-cell data
- Eleven grand challenges in single-cell data science
- Deep generative modeling for single-cell transcriptomics(Nov/2018)
- Jointly defining cell types from multiple single-cell datasets using LIGER(June/2019)
- scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data(Aug/2019)
- Fast, sensitive and accurate integration of single-cell data with Harmony(Nov/2019)
- BBKNN: fast batch alignment of single cell transcriptomes(Feb/2021)
- Joint probabilistic modeling of single-cell multi-omic data with totalVI(Feb/2021)
- scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis(Mar/2021)
- BABEL enables cross-modality translation between multiomic profiles at single-cell resolution(April/2021)
- Iterative single-cell multi-omic integration using online learning(Apr/2021)
- Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces(May/2021)
- Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities(May/2021)
- Integrated analysis of multimodal single-cell data(May/2021)
- Cobolt: Joint analysis of multimodal single-cell sequencing data(Jul/2021)
- ACE: Explaining cluster from an adversarial perspective(Jul/2021)
- From Clustering to Cluster Explanations via Neural Networks
- ENVI paper(keep focused)