Lin Wan

CPS maps physical coordinates to high-fidelity spatial transcriptomics using a multi-scale context distillation strategy, achieving state-of-the-art imputation and denoising performance.

DeepTracing disentangles lineage and intrinsic cellular variation in single-cell lineage tracing data, outperforming existing methods in clustering, phylogenetic recovery, and tumor-type classification.

scCausalVI disentangles intrinsic cellular states from treatment effects using a causality-aware generative model, outperforming existing methods in causal inference and multi-source data integration.

This paper introduces scProca, a deep generative model that integrates and generates single-cell proteomics data from transcriptomics using cross-attention mechanisms.

BiLinT jointly models lineage barcodes and gene expression to reconstruct high-resolution lineage trees with improved accuracy.

Graspot integrates spatial transcriptomics data using a graph attention network with optimal transport, improving alignment across datasets and revealing tumor microenvironment structures in breast cancer.

uniPort integrates single-cell multi-omics datasets using a coupled-VAE and Minibatch-UOT, enabling shared latent space embedding and reference atlas construction.

Pamona integrates heterogeneous single-cell multi-omics datasets by aligning cellular structures in a shared space while preserving dataset-specific features.

UnionCom integrates single-cell multi-omics datasets without cell or feature correspondence by aligning topological structures through matrix optimization and global scaling.

RobustClone recovers tumor subclone genotypes and infers evolutionary trees from noisy single-cell sequencing data, outperforming existing methods in accuracy and efficiency.

This study introduces CC-NMF, a framework that jointly infers clonal structure from matched scDNA and scRNA data, showing robustness and accuracy in resolving genomic and transcriptomic clonal architecture.

DensityPath visualizes and reconstructs cell developmental trajectories from large-scale scRNA-seq data using a level-set algorithm, identifying complex branching patterns in human preimplantation embryos.

This paper develops a statistical framework for connexel-wise analysis in brain-wide association studies, improving power and reducing false positives using spatial information from fMRI data.