Xianwen Ren

RedeViz enhances and visualizes spatial transcriptomics data with subcellular resolution, providing HE-stain-like visualization across multiple platforms and scales.

This study identifies the dominant modes of SARS-CoV-2 shedding in the human respiratory tract across different infection phases using metagenomic data and a novel algorithm.

RedeHist predicts spatial transcriptomics data from histology images at single-cell resolution using deep learning, outperforming existing methods in accuracy and gene imputation.

The paper constructs an immunoediting map of human cancers by analyzing single-cell data from 11057 tumor samples and 17382 healthy samples, revealing common immune cell state changes across cancer types.

The paper constructs a high-resolution immune map using single-cell deconvolution of RNA-seq samples from 30 tissues, revealing immune cell distributions across different sexes and ages.

The paper introduces SPRINTseq, a spatial transcriptomics method that enables fast, sensitive, and high-resolution data acquisition, revealing cellular and subcellular molecular architecture in Alzheimer's disease.

RedeFISH improves imaging-based spatial transcriptomics by simultaneously segmenting cells and imputing gene expression using single-cell RNA sequencing data.

Redeconve deconvolutes spatial transcriptomics data at single-cell resolution, outperforming existing methods in accuracy and speed across multiple datasets.

This study directly compares 10X Genomics Chromium and Smart-seq2 scRNA-seq platforms, finding differences in gene detection, noise, dropout rates, and differential expression, with recommendations for platform selection based on study goals.

This study uses single-cell RNA sequencing to analyze immune responses in COVID-19 patients, identifying key immune cell changes and molecular pathways linked to disease severity.

ROGUE quantifies cell cluster purity in scRNA-seq data, identifying subtypes and improving analysis of specific subpopulations across datasets.

This paper introduces CSOmap, a computational method to infer spatial organization from scRNA-seq data, successfully reconstructing tumor microenvironments and simulating perturbations to study cellular interactions.

SciBet is a Bayesian classifier for single-cell RNA sequencing data that predicts cell identity and can be deployed locally via a web client.

SSCC improves clustering accuracy and speed for single-cell RNA-seq data while reducing memory usage compared to existing methods.