.png){kind=icon}
返回
Unsupervised cell functional annotation for single-cell RNA-seq
.png){kind=icon}
OA
AI
DOI：10.1101/gr.276609.122.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the first steps in the analysis of single-cell RNA sequencing (scRNA-seq) data is the assignment of cell types. Although a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments, we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both low-dimensional representation for all genes and cell-specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-seq data sets from several different organs. We show, by using knowledge about gene sets, that UNIFAN greatly outperforms prior methods developed for clustering scRNA-seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster, making annotations easier.
期刊
IF:
5.5
论文数:
5.6K
被引数:
4.3W
机构
引用论文
From Louvain to Leiden: guaranteeing well-connected communities从鲁汶到莱顿: 保证良好连接的社区.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Supervised classification enables rapid annotation of cell atlases监督分类可以快速注释细胞图谱NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏MARS: discovering novel cell types across heterogeneous single-cell experiments火星: 在异质单细胞实验中发现新的细胞类型NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learningNATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Massively parallel digital transcriptional profiling of single cells单细胞的大规模并行数字转录谱分析NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏NWChem: A comprehensive and scalable open-source solution for large scale molecular simulationsNWChem: 大规模分子模拟的全面且可扩展的开源解决方案OAAI
OAAI分享收藏
查看更多内容