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GOWDL:基因本体学驱动的广泛和深度学习模型,用于scRNA-seq数据的细胞类型

Antonino Fiannaca¹, Massimo La Rosa¹, Laura La Paglia¹

¹ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy.

Briefings in bioinformatics

|September 27, 2023

概括

我们开发了一种基因本体学驱动的广泛和深度学习 (GOWDL) 模型,用于使用单细胞RNA测序 (scRNA-seq) 数据进行准确的细胞类型分类. 在识别不同组织的细胞群中,GOWDL的性能优于现有的方法.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

单细胞RNA测序 (scRNA-seq) 提供高分辨率的细胞数据.
准确的细胞类型识别对于了解组织组成至关重要.
现有的方法难以应对scRNA-seq数据的规模和复杂性.

研究的目的:

开发用于细胞类型分类的自动深度学习模型.
为了利用基因本体学功能注释和标记基因,提高准确性.
用最先进的方法来评估模型的性能.

主要方法:

实现混合广泛和深度学习架构 (GOWDL).
基因本体学 (GO) 功能数据和细胞类型特定标记基因的整合.
在五种组织类型中进行交叉验证和独立的外部测试.

主要成果:

在多种组织中,GOWDL实现了卓越的分类性能.
该模型的准确性很高,超过了其他12个预测器.
召回率很高 (92%),略低于表现最好的工具 (97%).

结论:

从scRNA-seq数据中进行细胞类型分类,GOWDL提供了一种强大而准确的方法.
该模型的混合架构有效地结合了功能和标记基因信息.
在乳腺癌免疫细胞分类的案例研究中证明了实用性.

关键词:

细胞类型的编号深度学习是一种深度学习.基因本体学是基因的本体学.标记基因是一种标记基因.这就是 scRNA-seqq.

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...