余弦相似度
鉴定(生物学)
计算生物学
可扩展性
基因
转录组
标记基因
计算机科学
生物
遗传学
人工智能
基因表达
模式识别(心理学)
植物
数据库
作者
Min Dai,Xiaobing Pei,Xiujie Wang
摘要
Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify true marker genes for different cell types still remains a big challenge. Here, we report COSine similarity-based marker Gene identification (COSG) as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets showed that the marker genes or genomic regions identified by COSG have greater cell-type specificity, demonstrating the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI