Decoding the Molecular Landscape of 262 Uterine Sarcomas: RNA-Seq Clustering of ESS, UTROSCT, and UUS with Prognostic Insights [0.03%]
262例子宫肉瘤的分子分型：基于RNA测序的ESS、UTR和UUS聚类及其预后意义
Jan Hojný,Jiří Dvořák,Romana Vránková et al.
Jan Hojný et al.
Low-grade endometrial stromal sarcomas (LG-ESS), high-grade endometrial stromal sarcomas (HG-ESS), undifferentiated uterine sarcomas (UUS), and uterine tumors resembling ovarian sex cord tumors (UTROSCT) are distinct non-smooth muscle cell ...
scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks [0.03%]
基于深度融合网络改进的单细胞RNA测序聚类方法
Tianxiang Liu,Cangzhi Jia,Yue Bi et al.
Tianxiang Liu et al.
Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-inc...
Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering [0.03%]
基于协同结构保护的单细胞RNA序列数据缺失值填补方法研究
Hang Gao,Wenjun Shen,Rui Li et al.
Hang Gao et al.
Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often co...
Zhaoyu Fang,Ruiqing Zheng,Min Li
Zhaoyu Fang
Motivation: Single-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq d...
Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets [0.03%]
癌症肿瘤数据集中单细胞RNA序列聚类算法的评估
Alaina Mahalanabis,Andrei L Turinsky,Mia Husić et al.
Alaina Mahalanabis et al.
Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires car...
Dirichlet process mixture models for single-cell RNA-seq clustering [0.03%]
基于狄利克雷过程混合模型的单细胞RNA序列聚类方法
Nigatu A Adossa,Kalle T Rytkönen,Laura L Elo
Nigatu A Adossa
Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no c...
Chenxing Zhang,Lin Gao,Bingbo Wang et al.
Chenxing Zhang et al.
Single-cell clustering is an important part of analyzing single-cell RNA-sequencing data. However, the accuracy and robustness of existing methods are disturbed by noise. One promising approach for addressing this challenge is integrating p...
Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation [0.03%]
基于深度监督、自监督和无监督学习的单细胞RNA测序聚类和注释方法集成研究
Liang Chen,Yuyao Zhai,Qiuyan He et al.
Liang Chen et al.
As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Mo...
Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering [0.03%]
在评估单细胞RNA测序聚类中考虑细胞类型层次结构的影响
Zhijin Wu,Hao Wu
Zhijin Wu
Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the struct...
Lihong Peng,Xiongfei Tian,Geng Tian et al.
Lihong Peng et al.
Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgrou...