Xiangjie Li

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

This paper introduces CarDEC, a deep learning model that simultaneously corrects batch effects, denoises, and clusters scRNA-seq data, outperforming existing methods in multiple evaluations.

Suppression of miR-199a-5p alleviates ulcerative colitis by upregulating endoplasmic reticulum stress component XBP1

This study shows that suppressing miR-199a-5p reduces ulcerative colitis by increasing XBP1 expression through endoplasmic reticulum stress.

Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

This paper introduces SpaGCN, a graph convolutional network that identifies spatial domains and spatially variable genes by integrating gene expression, spatial location, and histology in spatial transcriptomics data.

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis

ItClust improves clustering and cell type classification in scRNA-seq by using iterative transfer learning with neural networks, outperforming existing methods across diverse datasets.

Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis

This paper introduces DESC, a deep learning method for single-cell clustering that improves accuracy and removes batch effects.

The SARS-CoV-2 receptor ACE2 expression of maternal-fetal interface and fetalorgans by single cell transcriptome study

The paper investigates ACE2 expression in maternal-fetal interface and fetal organs using single-cell transcriptomics but was withdrawn due to insufficient analysis of ACE2 and TMPRSS2.