Shengquan Chen

CSFeatures improves identification of cell type-specific differential features in single-cell and spatial omics data

CSFeatures identifies cell type-specific differential features in single-cell and spatial omics data by considering expression levels, smoothness, and proportion across cell populations.

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.

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning

The paper introduces cpDistiller, a method to correct three technical effects in Cell Painting data using contrastive and domain-adversarial learning, improving analysis reliability and phenotypic response detection.

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.

Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective

The paper shows that using the median to impute missing values in single-cell DNA methylation data is effective and accurate for downstream analysis.

A multistage, multitask transformer-based framework for multi-disease diagnosis and prediction using personal proteomes

Prophet is a transformer-based framework that improves disease diagnosis and prediction using proteomics data from the UK Biobank, achieving high accuracy and identifying new protein-disease associations.

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.

A generic reference defined by consensus peaks for scATAC-seq data analysis

The paper constructs a generic chromatin accessibility reference (cPeaks) from bulk ATAC-seq data to improve scATAC-seq analysis and cell type identification.

stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics

stPlus enhances spatial transcriptomics by integrating scRNA-seq data, improving gene expression prediction and cell population identification.

OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions

OpenAnnotate provides a web server for efficiently annotating chromatin accessibility of genomic regions across biosamples, tissues, and systems.

A reference-guided approach for epigenetic characterization of single cells

This paper introduces RA3, a reference-guided method for analyzing single-cell chromatin accessibility data, showing improved performance across multiple datasets.

DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

DeepCAPE predicts enhancers using DNA sequences and DNase-seq data, outperforming existing methods and enabling cell line-specific identification.

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.

Simultaneous deep generative modeling and clustering of single cell genomic data

scDEC uses deep generative networks to simultaneously model and cluster scATAC-seq data, showing better performance than existing tools and enabling trajectory inference and cell type discovery.

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data

VPAC is a variational projection-based method for accurate single-cell transcriptomic clustering, validated across diverse datasets and showing robust performance.

SIP: An Interchangeable Pipeline for scRNA-seq Data Processing

SIP provides an interchangeable pipeline for scRNA-seq data processing, enabling easy integration and customization of analysis steps.