7 papers
scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing
Ping Xu, Pengjiang Li, Tian Du +6
Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. However, existing methods focus on mi…
scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
Ping Xu, Zaitian Wang, Zhirui Wang +5
Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance,…
scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Ping Xu, Zaitian Wang, Zhirui Wang +7
Single-cell RNA sequencing (scRNA-seq) technology enables systematic delineation of cellular states and interactions, providing crucial insights into cellular heterogeneity. Buildi…
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Ping Xu, Zhiyuan Ning, Pengjiang Li +5
Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially g…
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Ping Xu, Zhiyuan Ning, Meng Xiao +4
Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its p…
Soft Graph Clustering for single-cell RNA Sequencing Data
Ping Xu, Pengfei Wang, Zhiyuan Ning +3
Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq cluste…