Featured Publications
Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits, Nature Communications, 2024, 15(1),2383. [software]
We propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis.
Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications, 2023, 14(1), 296. [software][时空月速览]
We propose the use of a unified and principled probabilistic model, PRECAST, to simultaneously estimate low-dimensional embeddings for biological effects between cell/domain types, perform spatial clustering, and most importantly, align embeddings for normalized gene expression matrices from multiple tissue slides.
Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno. Nucleic Acids Research, 2023, 51(22), e115. [software]
We propose the use of a probabilistic model, SpatialAnno, which performs cell/domain-type assignments for SRT data and has the capability of leveraging non-marker genes to assign cell/domain types via a factor model while accounting for spatial information via a Potts model.
A Deep Generative Approach to Conditional Sampling. Journal of the American Statistical Association, 2023, 118(543):1837-1848.
Generalized factor model for ultra-high dimensional correlated variables with mixed types. Journal of the American Statistical Association, 2023, 118(542): 1385-1401.
Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology. Nature Communications, 2022, 13(1): 6490. [software]
We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty.
Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data. Nucleic Acids Research, 2022, 50(12): e72. [software]
We propose a unified and principled method to both estimate low-dimensional embeddings relevant to latent class labels and, in the case of spatial transcriptomics analysis, further leverage these embeddings with spatial information to perform spatial clustering using an HMRF.
A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Research, 2020, 48(19): e109.
We propose a tissue-specific collaborative mixed model (TisCoMM) for TWASs, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model.