Publications

2026

• Protocol for detecting cancer in blood using targeted DNA methylation
  Zhaoyu Jiang*, Yuqing Guo*, Yongqu Lu*, Kun Qian*, Renjie Luo, Xiaomeng Liu, Jie Ren, Wei Fu, Lu Wen, Xin Zhou
  STAR Protocols, 2026

  We present a step-by-step tMCTA-seq workflow for detecting and distinguishing colorectal and gastric cancers from plasma cell-free DNA. The protocol covers sample preparation, targeted methylation sequencing, and computational analysis, with guidance for adapting the workflow to other cancer types.

• Leveraging Spot–Gene Heterogeneous Graphs for Unified Spatially Resolved Transcriptomics Domain Detection on Single-Slice and Multi-Slice Data
  Lina Xia, Zhenyue Ding, Xun Zhang, Kun Qian, Hongwei Li
  Genes, 2026

  We introduce stHGCL, a framework that combines a spot–gene heterogeneous graph with spatial graph learning and contrastive learning to identify spatial domains. It supports both individual tissue slices and joint analysis of multiple slices, including datasets without prior spatial alignment.

• The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics
  Junjie Tang*, Zihao Chen*, Kun Qian*, Siyuan Huang, Yang He, Shenyi Yin, Xinyu He, Buqing Ye, Yan Zhuang, Hongxue Meng, Jianzhong Jeff Xi, Ruibin Xi
  Nature Cell Biology, 2026

  We introduce SpaHDmap, an interpretable dimension reduction framework that enhances spatial resolution by integrating ST gene expression with high-resolution histology images. SpaHDmap incorporates non-negative matrix factorization into a multimodal fusion encoder-decoder architecture, enabling the identification of interpretable, high-resolution embeddings.

2025

• A targeted DNA methylation method for detecting gastrointestinal cancer in circulating cell-free DNA
  Zhaoyu Jiang*, Yuqing Guo*, Yongqu Lu*, Kun Qian*, Xiaomeng Liu, Siyi Lu, Jie Ren, Fuchou Tang, Wei Fu, Lu Wen, Xin Zhou
  iScience, 2025

  We developed tMCTA-seq, a cost-effective targeted DNA methylation method for simultaneous detection and differentiation of colorectal and gastric cancers. By integrating a universal nested primer design with an ensemble machine learning framework, we achieved high diagnostic performance across multiple cancer stages in clinical cfDNA samples.

2024

• Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network
  Xun Zhang*, Kun Qian*, Hongwei Li
  Briefings in Bioinformatics, 2024

  By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, we proposed a structure-preserved scRNA-seq data integration approach scHetG.

• Spatial transcriptomics: a new frontier in cancer research
  Siyuan Huang*, Linkun Ouyang*, Junjie Tang, Kun Qian, Xuanwei Chen, Zijie Xu, Jingsi Ming, Ruibin Xi
  Clinical Cancer Bulletin, 2024

  We review spatial transcriptomics technologies and their applications to tumor heterogeneity and the tumor microenvironment. The article also outlines computational analysis workflows, provides an accompanying analysis tutorial, and discusses opportunities for spatial multi-omics in cancer research.

• Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation
  Renjing Liu*, Kun Qian*, Xinwei He, Hongwei Li
  BMC Bioinformatics, 2024

  To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar scRNA-seq data.

• scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding
  Ting Li*, Kun Qian*, Xiang Wang, Wei Vivian Li, Hongwei Li
  NAR Genomics and Bioinformatics, 2024

  We introduce scBiG, a novel graph node embedding method designed for representation learning in scRNA-seq data. scBiG establishes a bipartite graph connecting cells and expressed genes, and then constructs a multilayer graph convolutional network to learn cell and gene embeddings.

2023

• scAce: an adaptive embedding and clustering method for single-cell gene expression data
  Xinwei He*, Kun Qian*, Ziqian Wang, Shirou Zeng, Hongwei Li, Wei Vivian Li
  Bioinformatics, 2023

  We propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance.

2022

• scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
  Kun Qian*, Shiwei Fu, Hongwei Li, Wei Vivian Li*
  Genome Biology, 2022

  Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples.

* Equal contribution