Feng Zeng

This study maps DNA methylation changes in 12 organs/tissues of spaceflight mice, showing tissue-specific and reversible epigenetic modifications linked to metabolic and immune dysregulation.

This study benchmarks five scCNV inference methods across multiple scRNA-seq platforms and mixed samples, finding variations in performance based on data characteristics and batch effects.

SUREv2 compresses single-cell atlases while preserving distribution, enabling privacy-protected data sharing and novel cell type discovery.

SURE establishes a standardized cell coordinate system for cell identification using metacells, enabling atlas functionalities and integration of large-scale datasets.

This paper introduces ISMI-VAE, a deep learning model that integrates SNV and gene expression data to classify disease cells, showing improved effectiveness and interpretability.

OmniClustifyXMBD identifies putative cell states across diverse single-cell omics datasets by integrating adaptive signal isolation with a deep variational Gaussian mixture model clustering algorithm.

TREVIXMBD integrates scRNA-seq and scATAC-seq data to model gene regulation, improving cell typing and batch correction while revealing TF-gene regulation variations.

This paper introduces scPhenoXMBD, a deep auto-regressive factor model that extracts biological signals from single-cell transcriptomics data, improving analysis reliability and enabling better identification of IFNβ-responsive pathways.

scPheno integrates scRNA-seq with disease phenotypes to identify transcriptional variations linked to COVID-19 diagnosis and severity, achieving 81.3% accuracy using 4 genes.

ProbAlign is a re-alignment tool for long sequencing reads that improves variant calling and viral quasispecies reconstruction by reducing false positives and increasing true positives.