Single-cell Genomics

We develop computational tools for single cell analysis that capture cell hetergeneity, cell state transition, cell-cell/environment communications/response etc. Current reserach interests lie on identifying/discovering biological mechanisms to analyze time‑course data, lineage tracing, CRISPR and small‑molecule perturbation screens. Our models provide insights into gene regulation mechanisms by modeling cell development, transition, response, aging, etc.

Related Papers

Erpai Luo, Minsheng Hao, Lei Wei, Xuegong Zhang, Qiao Liu (2026). A Multi-modal Diffusion Model with Dual-Cross-Attention for Multi-Omics Data Generation and Translation. Nature Communications.

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Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, Shaoting Zhang (2022). Deep generative modeling and clustering of single cell Hi-C data. Briefings in Bioinformatics, 24(1), bbac494, 2023.

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Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong (2021). Simultaneous deep generative modelling and clustering of single-cell genomic data. Nature Machine Intelligence, 3(6), 536-544, 2021.

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