版权说明 操作指南
首页 > 成果 > 详情

scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Yi-Xuan Xiong;Xiao-Fei Zhang*
通讯作者:
Xiao-Fei Zhang
作者机构:
School of Mathematics and Statistics, Central China Normal University , Wuhan 430079 , China
Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University , Wuhan 430079 , China
[Yi-Xuan Xiong; Xiao-Fei Zhang] School of Mathematics and Statistics, Central China Normal University , Wuhan 430079 , China<&wdkj&>Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University , Wuhan 430079 , China
通讯机构:
[Xiao-Fei Zhang] S
School of Mathematics and Statistics, Central China Normal University , Wuhan 430079 , China<&wdkj&>Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University , Wuhan 430079 , China
语种:
英文
关键词:
cell-type annotation;novel cell-type identification;optimal transport;distance metric learning;single-cell RNA sequencing
期刊:
BRIEFINGS IN BIOINFORMATICS
ISSN:
1467-5463
年:
2024
卷:
25
期:
2
基金类别:
This work was supported by the National Natural Science Foundation of China (12271198 and 11871026).
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学学院
摘要:
The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previous...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com