Xiaoyang Chen

Hi-Cformer enables multi-scale chromatin contact map modeling for single-cell Hi-C data analysis

Hi-Cformer models multi-scale chromatin contact maps from single-cell Hi-C data, improving cell type separation and imputation of 3D genome features.

Cross-modality representation and multi-sample integration of spatially resolved omics data

This paper introduces PRESENT, a contrastive learning framework for integrating spatially resolved multi-omics data across samples and modalities, showing improved performance in detecting spatially functional domains and biological regulatory mechanisms.

Human-scATAC-Corpus: a comprehensive database of scATAC-seq data

The paper introduces Human-scATAC-Corpus, a database of human scATAC-seq data, and presents a foundation model for epigenomic analysis.

CREATE: cell-type-specific cis-regulatory elements identification via discrete embedding

CREATE identifies cell-type-specific cis-regulatory elements using a deep learning model that integrates genomic sequences, chromatin accessibility, and chromatin interactions.

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

INSTINCT integrates multi-sample spatial chromatin accessibility sequencing data by removing batch effects and noise, enabling comprehensive analysis of epigenomic landscapes.

EpiAgent: Foundation model for single-cell epigenomic data

EpiAgent is a foundation model for single-cell epigenomic data that excels in feature extraction, cell annotation, and data imputation.

Detection of spatial chromatin accessibility patterns with inter-cellular correlations

This paper introduces Descartes, a model for identifying spatially variable chromatin accessibility peaks using inter-cellular correlations, showing improved performance and efficiency over existing methods.

Cofea: correlation-based feature selection for single-cell chromatin accessibility data

Cofea selects biologically relevant features from single-cell chromatin accessibility data by focusing on correlations between accessible regions, improving downstream analysis.

PAST: latent feature extraction with a Prior-based self-Attention framework for Spatial Transcriptomics

PAST characterizes spatial domains in spatial transcriptomics data using a variational graph convolutional auto-encoder with prior-based self-attention, improving downstream analyses.

scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

scEpiTools is a database that organizes and recommends tools for analyzing single-cell epigenomic data, including an ensemble method for chromatin accessibility analysis.

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

simCAS generates high-fidelity scCAS data using embeddings, enabling benchmarking of computational methods for cell clustering, trajectory inference, and cis-regulatory interaction analysis.

EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

EnClaSC is an ensemble method for cell-type classification in scRNA-seq data, showing high accuracy and robustness across datasets and species.