Xiang Zhou

TipQUANT quantifies spatiotemporal dynamics of fluorescent molecules in tip-growing cells, improving analysis efficiency and accuracy.

This paper introduces STACAME, a framework for analyzing cross-species spatial transcriptomics data to identify shared and divergent tissue architectures across species.

This paper introduces STAMO, a spatially aware integration method for unpaired spatial multi-omics data, showing improved alignment and domain detection compared to existing methods.

This paper identifies four major challenges in single-cell differential expression analysis and proposes a novel paradigm to address them.

This paper develops STAligner, a graph attention neural network for integrating spatial transcriptomics data across different conditions, technologies, and developmental stages, identifying shared and specific spatial domains.

scAdapt improves single-cell RNA-seq classification across platforms and species by aligning domain distributions using adversarial learning.

The paper develops a deep learning framework to integrate multi-omics data for cancer prognosis, showing improved risk prediction across 15 cancers and identifying prognostic markers in breast cancer.

This paper introduces GraphSCI, a method that imputes dropout events in scRNA-seq data using graph convolution networks, outperforming existing methods on simulated and real data.

GraphSCC improves single-cell clustering by incorporating structural relations between cells using graph convolutional networks and a dual self-supervised module.

The paper introduces TDimpute, a transfer learning-based deep neural network for imputing missing RNA-seq data using DNA methylation, showing improved accuracy across 16 cancer datasets.