This paper benchmarks algorithms for identifying spatially variable genes in spatial transcriptomics using synthesized and real-world datasets to evaluate their effectiveness and application scenarios.

Bioinformatics · Mar 26, 2025Peer-reviewedBioinformatics

Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno

Xingjie Shi, Yi Yang, Xiaohui Ma, Yong Zhou, Zhenxing Guo, Chaolong Wang, Jin Liu

SpatialAnno improves spatial transcriptomics annotation by integrating non-marker genes and spatial information without a reference dataset.

Nucleic Acids Research · Nov 6, 2023Peer-reviewedBioinformatics

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Poh-Sheng Yeong, Jin Liu

PRECAST integrates spatial transcriptomics data from multiple sources, improving cell and domain detection through probabilistic embedding, clustering, and alignment.

Nature Communications · Jan 18, 2023Peer-reviewedGenomics

Joint dimension reduction and clustering analysis for single-cell RNA-seq and spatial transcriptomics data

Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Poh-Sheng Yeong, Xiang Zhou, Xingjie Shi, Jin Liu

This paper introduces DR-SC, a method that jointly performs dimension reduction and spatial clustering for single-cell and spatial transcriptomics data, improving accuracy and biological relevance.

Nucleic Acids Research · Mar 22, 2022Peer-reviewedGenomics

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Yi Yang, Xingjie Shi, Wei Liu, Qiuzhong Zhou, Mai Chan Lau, Jeffrey Chun Tatt Lim, Lei Sun, Cedric Chuan Young Ng, Joe Poh-Sheng Yeong, Jin Liu

SC-MEB is a spatial clustering method for transcriptomics data that outperforms existing methods in clustering performance and scalability.

Briefings in Bioinformatics · Oct 13, 2021Peer-reviewedGenomics

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Xingjie Shi, Xiaoran Chai, Yi Yang, Qing Cheng, Yuling Jiao, Haoyue Chen, Jian Huang, Can Yang, Jin Liu

This paper introduces TisCoMM, a tissue-specific collaborative mixed model for TWAS that improves gene-trait association detection and identifies tissue-specific gene roles in complex traits.

Nucleic Acids Research · Sep 3, 2020Peer-reviewedBioinformatics

MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

Qing Cheng, Yi Yang, Xingjie Shi, Kar-Fu Yeung, Can Yang, Heng Peng, Jin Liu

This paper introduces MR-LDP, a Mendelian randomization method that accounts for linkage disequilibrium and horizontal pleiotropy, showing improved causal inference in GWAS summary statistics.

NAR Genomics and Bioinformatics · May 4, 2020Peer-reviewedGenetics

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

Yi Yang, Xingjie Shi, Yuling Jiao, Jian Huang, Min Chen, Xiang Zhou, Lei Sun, Xinyi Lin, Can Yang, Jin Liu

CoMM-S2 uses GWAS summary statistics to infer mechanistic roles of genetic variants in gene expression, achieving performance comparable to methods using individual-level data.

Bioinformatics · Nov 21, 2019Peer-reviewedBioinformatics