Liang Li Wu

SpaSEG is a convolutional neural network model for analyzing spatially resolved transcriptomics data, showing superior reliability and efficiency in detecting spatial patterns and intercellular interactions.

spaTrack infers spatial cell trajectories using optimal transport, revealing spatiotemporal dynamics in regeneration and tumor development.

This paper shows that LINE1 RNA is essential for maintaining hESCs by preventing the 8C state through nuclear compartmentalization and PRC2-mediated repression.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

CDCP is a platform for sharing and analyzing single-cell datasets, offering visualization and analysis tools for researchers.

The paper uses Stereo-seq to map single-cell transcriptomes of axolotl telencephalon during development and regeneration, identifying a key radial glial cell subtype involved in early wound healing.

The paper introduces DESJ-detection, a tool for analyzing differential splicing in single-cell RNA sequencing data, and identifies splicing differences in liver tumor-infiltrating T cells.

The paper uses single-cell RNA-seq to study liver and gallbladder development from the embryonic endoderm, identifying key transcription factors and gene expression changes during differentiation.

The paper integrates single-cell ATAC and transcriptomic data with spatial transcriptomics to map regulatory features and disease vulnerabilities in macaque cortex.

The paper creates an ATAC-seq atlas of chromatin accessibility in adult mouse tissues, identifying tissue-specific accessible elements and transcription factor motifs.

The paper introduces scCAT-seq, a method to simultaneously assay chromatin accessibility and transcriptome in single cells, and demonstrates its application in human pre-implantation embryos to reveal regulatory heterogeneity.

This study compares the sensitivity and accuracy of the BGISEQ-500 and Illumina HiSeq platforms for single-cell RNA sequencing using a standardized resource of 468 unique single-cells and 1,297 matched single cDNA samples.

This study uses single-cell RNA-seq to analyze transcriptome dynamics during human early neural differentiation, identifying novel transcription factors and regulatory elements involved in the process.