Yunhe Liu

Integration of spatial single-cell proteomics and spatial metabolomics reveals tumor microenvironment predictive of immunotherapy response in mucosal melanoma

UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

Toward Computationally Complete Spatial Omics

COSIE is a computational framework that integrates multiple spatial omics modalities to generate high-resolution molecular landscapes, outperforming existing methods across diverse datasets.

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

UTOPIA quantifies confidence in virtual spatial transcriptomics predictions across scales, improving reliability and preventing false conclusions.

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

REMAP reconstructs spatial tissue architecture from scRNA-seq using deep learning and ST references, revealing microenvironmental features and cancer subtypes.

An Interpretable Multi-instance Learner Decodes Cellular Recruitment from Spatially Resolved Transcriptomics

This study introduces a deep learning model called spacer to analyze spatially resolved transcriptomics data, revealing novel insights into cellular recruitment mechanisms in tumors and heart tissue during myocarditis.

Designing smart spatial omics experiments with S2Omics

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

Optimization and redevelopment of single-cell data analysis workflow based on deep generative models

The paper improves scRNA-seq analysis by using deep generative models to identify and remove noise cells, enhancing clustering and gene analysis consistency.

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

The paper introduces an attention-based deep multiple instance learning model for circRNA identification from lncRNA, showing improved performance and identifying key sequence motifs.

Single-cell transcriptome profiling simulation reveals the impact of sequencing parameters and algorithms on clustering

This study simulates scRNA-seq data to evaluate how sequencing parameters and clustering algorithms affect cell clustering accuracy.