Chaoyong James Yang

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Decoder-FFPE-seq improves sensitivity and resolution for spatial transcriptomics in FFPE tissues, enabling analysis of rare genes and tumor microenvironments.

Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics

Spaceland reconstructs high-resolution 3D molecular atlases from sparse spatial transcriptomics and histology, improving resolution and accuracy across multiple biological contexts.

SpatioCell: Deep Integration of Histology and Spatial Transcriptomics for Profiling the Cellular Microenvironment at Single-Cell Level

SpatioCell integrates histology and spatial transcriptomics to extract single-cell resolution cell type and expression information from ST data, revealing microenvironment features in triple negative breast cancer.

scBiopsy-seq: a platform for temporal single-cell RNA-seq analysis

scBiopsy-seq enables temporal single-cell RNA-seq analysis with high detection diversity and reliability, revealing key roles of fatty acid beta oxidation in transcriptional suppression.

Well-Paired-Seq2: High-Throughput and High-Sensitivity Strategy for Characterizing Low RNA-Content Cell/Nucleus Transcriptomes

The paper introduces WPS2, a high-throughput and high-sensitivity scRNA-seq method, which improves gene and transcript detection in low RNA-content cells and nuclei.

mitoSplitter: A mitochondrial variants-based method for efficient demultiplexing of pooled single-cell RNA-seq

mitoSplitter uses mitochondrial RNA variants to demultiplex pooled single-cell RNA-seq data, enabling efficient analysis of multiple samples without batch effects.

Dendrimeric DNA Coordinate Barcoding Design for Spatial RNA Sequencing

Decoder-seq is a spatial RNA sequencing method that provides high-resolution, cost-effective, and sensitive spatial transcriptomic analysis, revealing detailed spatial gene expression patterns in mouse hippocampus and renal cancer.

Single-cell transcriptome lineage tracing of human pancreatic development identifies distinct developmental trajectories of alpha and beta cells

This study uses single-cell RNA-seq and mitochondrial variants to trace lineage differences between alpha and beta cells in human pancreatic development, revealing distinct developmental trajectories not seen in mice.

SuperCT: A supervised-learning-framework to enhance the characterization of single-cell transcriptomic profiles

SuperCT improves single-cell transcriptomic analysis by using a supervised classifier to accurately identify cell types from RNA expression profiles, outperforming traditional unsupervised methods.