IRIS project

                      scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (2021)

                   Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma & Dong Xu

• Hypothesis-free deep learning framework

• Cell-cell relationships with graph neural networks

• Left-truncated mixture Gaussian model

• Gene imputation and cell clustering

                      DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer (2022)

                   Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma & Dong Xu

​Single-cell multi-omics (scMulti-omics) allows for the generation and quantification of multiple modalities simultaneously to fully capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Such technology reshapes the investigation of cellular heterogeneity and yields novel insights in neuroscience, cancer biology, immuno-oncology, and therapeutic responsiveness. We seek to develop an end-to-end and hypotheses-free deep learning framework (DeepMAPS) to take the advantage of heterogeneous graphs and graph transformers in elucidating cellular heterogeneity and inferring cell-type-specific biological networks.