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主题词1：单细胞RNA测序数据
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Inferred developmental origins of brain tumors from single-cell RNA-sequencing data [0.03%] 基于单细胞RNA测序数据推断脑肿瘤的发育起源
Su Wang,Rachel Naomi Curry,Malcolm F McDonald et al. Su Wang et al.
Background: The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated d...
scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready [0.03%] scCompass：一个集成的多物种单细胞 RNA 测序数据库（支持人工智能）
Pengfei Wang,Wenhao Liu,Jiajia Wang et al. Pengfei Wang et al.
Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by ...
GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data [0.03%] GeneDX-PBMC：一种利用血液单细胞RNA测序数据解锁阿尔茨海默病生物标志物的对抗自编码器框架
Hediyeh Talebi,Shokoofeh Ghiam,Asiyeh Mirzaei Koli et al. Hediyeh Talebi et al.
Objective: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced dee...
scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data [0.03%] 用于scRNA_seq数据的改进深度稀疏子空间聚类方法(scPEDSSC)
Xiaopeng Wei,Jingli Wu,Gaoshi Li et al. Xiaopeng Wei et al.
It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of ...
Integration of Microarray and Single-Cell RNA-Seq Data and Machine Learning Allows the Identification of Key Histone Modification Gene Changes in Spermatogonial Stem Cells [0.03%] 微阵列和单细胞RNA测序数据及机器学习的整合可鉴定支持细精管干细胞关键组蛋白修饰基因的变化
Ali Shakeri Abroudi,Hossein Azizi,Melika Djamali et al. Ali Shakeri Abroudi et al.
Histone modifications play a critical role in regulating gene expression and maintaining the functionality of spermatogonial stem cells (SSCs), which are essential for male fertility and spermatogenesis. In this study, we integrated microar...
ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction [0.03%] 基于自适应聚合机制的图卷积网络ScAGCN用于单细胞数据降维
Xiaoshu Zhu,Liquan Zhao,Fei Teng et al. Xiaoshu Zhu et al.
With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation...
High-resolution single-cell RNA-seq data and heterogeneity analysis of human ESCs and ffEPSCs [0.03%] 高分辨率单细胞RNA测序数据及人类多能干细胞异质性分析
Lihang Zhu,Ran Zheng,Shanshan Wen et al. Lihang Zhu et al.
This study presents a comprehensive transcriptomic analysis of feeder-free extended pluripotent stem cells (ffEPSCs) and their parental human embryonic stem cells (ESCs), providing new insights into understanding human early development and...
A Multi-omics approach to identify and validate shared genetic architecture in rheumatoid arthritis, multiple sclerosis, and type 1 diabetes: integrating GWAS, GEO, MSigDB, and scRNA-seq data [0.03%] 多组学方法识别和验证类风湿性关节炎、多发性硬化症和1型糖尿病共有的遗传结构：整合GWAS、GEO、MSigDB和单细胞RNA测序数据
Tailin Wang,Qian He,Kei Hang Katie Chan Tailin Wang
The notable comorbidity among autoimmune diseases underscores their shared genetic underpinnings, particularly evident in rheumatoid arthritis (RA), type 1 diabetes (T1D), and multiple sclerosis (MS). However, the exact components and mecha...
GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data [0.03%] 基于图表示的单细胞RNA测序数据推断基因调控网络的新方法(GRLGRN)
Kai Wang,Yulong Li,Fei Liu et al. Kai Wang et al.
Background: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investig...
Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data [0.03%] 深度学习驱动的单细胞RNA测序数据中细胞类型标签传输协议
Zoe Zabetian,Jesus Gonzalez-Ferrer,Julian Lehrer et al. Zoe Zabetian et al.
Here, we present a protocol for using SIMS (scalable, interpretable machine learning for single cell) to transfer cell type labels in single-cell RNA sequencing data. This protocol outlines data preparation, model training with labeled data...
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