BioX Future Interdisciplinary Technology Mathematics Information Life Sciences

[29] Qun Jiang†, Shengquan Chen†, Xiaoyang Chen, Rui Jiang*. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics, 2024, Accepted. [link, code]

[28] Zijing Gao, Rui Jiang, Shengquan Chen*. OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regions. Bioinformatics Advances, 2024, vbae055. [link, code]

[27] Sijie Li, Yuxi Li, Yu Sun, Yaru Li, Xiaoyang Chen, Songming Tang, Shengquan Chen*. EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data. Bioinformatics, 2024, btae191. [link, code]

[26] Yichuan Cao, Xiamiao Zhao, Songming Tang, Qun Jiang, Sijie Li, Siyu Li, Shengquan Chen*. scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders. Nature Communications, 2024, 15:2973. [link, code]

[25] Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen*, Rui Jiang*. Discrete latent embedding interpretably unveils cell heterogeneity for single-cell chromatin accessibility sequencing data. Nature Computational Science, 2024, Accepted. [link, code]

[24] Yuhang Jia†, Siyu Li†, Rui Jiang, Shengquan Chen*. Accurate annotation for differentiating and imbalanced cell types in single-cell chromatin accessibility data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, Online. [link, code]

[23] Songming Tang, Xuejian Cui, Rongxiang Wang, Sijie Li, Siyu Li, Xin Huang, Shengquan Chen*. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nature Communications, 2024, 15:1629. [link, code]

[22] Haixin Wang†, Yunhan Wang†, Qun Jiang†, Yan Zhang*, Shengquan Chen*. SCREEN: predicting single-cell gene expression perturbation responses via optimal transport. Frontiers of Computer Science, 2024, Online. [link, code]

[21] Keyi Li†, Xiaoyang Chen†, Shuang Song, Lin Hou, Shengquan Chen*, Rui Jiang*. Cofea: correlation-based feature selection for single-cell chromatin accessibility data. Briefings in Bioinformatics, 2024, bbad458. [link, code]

[20] Wenhao Zhang, Rui Jiang, Shengquan Chen*, Ying Wang*. scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data. Genome Biology, 2023, 24:225. [link, code]

[19] Zijing Gao†, Xiaoyang Chen†, Zhen Li†, Xuejian Cui†, Qun Jiang, Keyi Li, Shengquan Chen*, Rui Jiang*. scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data. Journal of Genetics and Genomics, 2023, Online. [link, tool]

[18] Zhen Li, Xiaoyang Chen, Xuegong Zhang, Rui Jiang, Shengquan Chen*. Latent feature extraction with a Prior-based self-Attention framework for Spatial Transcriptomics. Genome Research, 2023, Online. [link, code]

[17] Chen Li†, Xiaoyang Chen†, Shengquan Chen, Rui Jiang*, Xuegong Zhang*. simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data. Bioinformatics, 2023, btad453. [link, code]

[16] Siyu Li, Songming Tang, Yunchang Wang, Sijie Li, Yuhang Jia, Shengquan Chen*. Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance. Quantitative Biology, 2023, Online. [link, code]

[15] Rui Jiang, Zhen Li, Yuhang Jia, Siyu Li, Shengquan Chen*. SINFONIA: scalable identification of spatially variable genes for deciphering spatial domains. Cells, 2023, 12(4):604. [link, code]

[14] Shengquan Chen*, Rongxiang Wang, Wenxin Long, Rui Jiang*. ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data. Bioinformatics, 2022, btac842. [link, code]

[13] Zheng Zhang, Shengquan Chen, Zhixiang Lin*. RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics, 2022, bbac540. [link, code]

[12] Xiaoyang Chen†, Shengquan Chen†, Shuang Song, Zijing Gao, Lin Hou, Xuegong Zhang, Hairong Lv, Rui Jiang*. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence, 2022, 4:116-126. [link, code]

[11] Xi Xi, Haochen Li, Shengquan Chen, Tingting Lv, Tianxing Ma, Rui Jiang, Ping Zhang, Wing Hung Wong*, Xuegong Zhang*. Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling. iScience, 2022, 25(8):104790. [link, code]

[10] Shengquan Chen†, Guanao Yan†, Wenyu Zhang, Jinzhao Li, Rui Jiang*, Zhixiang Lin*. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications, 2021, 12:2177. [link, code]

[9] Shengquan Chen, Qiao Liu, Xuejian Cui, Zhanying Feng, Chunquan Li, Xiaowo Wang, Xuegong Zhang, Yong Wang, Rui Jiang*. OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. Nucleic Acids Research, 2021, 49(W1):W483-W490. [link, tool]

[8] Wanwen Zeng†, Shengquan Chen†, Xuejian Cui†, Xiaoyang Chen, Zijing Gao, Rui Jiang*. SilencerDB: a comprehensive database of silencers. Nucleic Acids Research, 2021, 49(D1):D221-D228. [link, tool]

[7] Shengquan Chen, Mingxin Gan, Hairong Lv, Rui Jiang*. DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers. Genomics, Proteomics & Bioinformatics, 2021, 19(4):565-577. [link, code]

[6] Shengquan Chen, Boheng Zhang, Xiaoyang Chen, Xuegong Zhang, Rui Jiang*. stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. ISMB/ECCB 2021, Bioinformatics, 2021, 37(S1):i299-i307. [link, tool]

[5] Qiao Liu, Shengquan Chen, Rui Jiang*, Wing Hong Wong*. Simultaneous deep generative modeling and clustering of single cell genomic data. Nature Machine Intelligence, 2021, 3:536-544. [link, code]

[4] Xiaoyang Chen, Shengquan Chen, Rui Jiang*. EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes. BMC Bioinformatics, 2020, 21(13):1-16. [link, code]

[3] Shengquan Chen, Kui Hua, Hongfei Cui, Rui Jiang*. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data. BMC Bioinformatics, 2019, 20(7):139-151. [link, code]

[2] Shaoming Song, Hongfei Cui, Shengquan Chen, Qiao Liu, Rui Jiang*. EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information. Quantitative Biology, 2019, 7(3):233-243. [link, tool]

[1] Xu Min†, Wanwen Zeng†, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang*. Predicting enhancers with deep convolutional neural networks. BMC Bioinformatics, 2017, 18(13):478. [link, code]