Weiman Yuan

OncoSpot enables benchmarking of cancer spatial transcriptomics methods across tumor evolutionary modes

OncoSpot benchmarks cancer spatial transcriptomics methods across tumor evolutionary modes by simulating realistic cancer data with known subclonal and copy-number features.

CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancergenomes

CNVeil infers haplotype-specific copy number and subclonal architecture from single-cell cancer genomes, outperforming existing methods in accuracy and resolution.

Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant

DeepMalignant identifies malignant cells in scRNA-seq data by integrating gene expression and CNA, outperforming existing methods across multiple cancer types and platforms.

Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states

This study benchmarks 20 cell type annotation methods on spatial transcriptomics data to evaluate their performance in resolving cell types, subtypes, and biological organization across diverse datasets.

From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation

FEAST generates synthetic spatial transcriptomics data with tunable spatial and transcriptional variation for benchmarking and 3D reconstruction.

BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics

BioLACE improves spatial clustering in transcriptomics by integrating spatial geometry and marker gene priors within a VAE framework, achieving better cell type accuracy and interpretability.

MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics

MaskGraphene integrates multi-slice, multi-condition spatial transcriptomics data using a graph neural network to generate interpretable joint embeddings with preserved geometric information, improving downstream analyses.

CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data

CNVeil improves tumor subclone identification and copy number estimation in single-cell DNA sequencing by addressing noise and bias through novel computational methods.