Mingyao Li

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

This study integrates single-cell spatial transcriptomics with bulk and single-cell RNA sequencing to reveal the molecular heterogeneity and mechanisms of atherosclerotic carotid artery plaques.

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

iSCALE predicts super-resolution gene expression and annotates tissue architecture in large tissues, revealing features missed by conventional spatial transcriptomics.

The paper evaluates segmentation methods in spatial transcriptomics, showing that manual review is necessary due to model-specific errors.

The paper introduces spatial-Mux-seq, a multi-modal spatial technology that profiles five modalities in tissues, revealing new insights into cell types, gene regulation, and epigenetic gradients in mouse embryos and brains.

This study benchmarks seven methods for integrating unpaired and paired scRNA-seq and snATAC-seq data, finding that multiome data improve cell type annotation but require sufficient cell numbers.

Tempo is a Bayesian method for inferring circadian phase from scRNA-seq data, providing accurate estimates and uncertainty quantification.

The paper maps genetic variants to cell types in the kidney to identify disease mechanisms and drug targets.

The paper maps the regulatory landscape of the mouse kidney at single cell resolution, identifying key transcription factors and regulatory circuits, and linking variants to kidney disease.

SCATS detects differential alternative splicing in scRNA-seq data with or without UMIs, showing higher sensitivity than existing methods and validated by qRT-PCR.

The paper uses single-cell and bulk RNA-seq to analyze gene expression changes in human retina with AMD, identifying cell type-specific alterations.

The paper identifies PPP1R3A as a central cardioprotective factor in pressure overload heart failure by analyzing gene network rewiring in human hearts.