使用CrossmodalNet对时间解析的单细胞基因蛋白表达的可解释建模
Yongjian Yang1, Yu-Te Lin2, Guanxun Li3
1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
Briefings in bioinformatics
|October 5, 2023
概括
通过单细胞RNA测序数据,CrossmodalNet准确地预测细胞表面蛋白质的表达. 这种可解释的机器学习模型揭示了因果基因-蛋白质关系,推进了CITE-seq分析.
科学领域:
- 单细胞生物学 单细胞生物学
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
背景情况:
- 细胞表面蛋白对于细胞功能和治疗向至关重要.
- CITE-seq同时测量基因和表面蛋白质的表达,但成本高且复杂.
- 从基因数据预测蛋白质表达的现有计算方法是资源密集型的,缺乏可解释性.
研究的目的:
- 开发一种可解释的机器学习模型,用于从单细胞RNA测序 (scRNA-seq) 数据中预测表面蛋白质表达.
- 为了解决当前预测方法的计算需求和缺乏可解释性.
- 为了更深入地了解涉及细胞表面蛋白质的分子机制.
主要方法:
- 开发了一个可解释的机器学习模型CrossmodalNet.
- 使用定制的自适应性损失函数来准确预测表面蛋白质丰度.
- 包含时间信息编码,用于特定时间点的预测和因果关系发现.
主要成果:
- 交叉模式网络从scRNA-seq数据准确地预测了表面蛋白质的丰度.
- 该模型有效地编码时间信息用于时间解析分析.
- 确定了无噪声的因果基因-蛋白质关系,提高了可解释性.
- 在三个公开的CITE-seq数据集上与基准分析方法验证了性能.
结论:
- 交叉模式网络提供了一种准确和可解释的方法,用于使用scRNA-seq数据进行表面蛋白质表达的分析.
- 该模型增强了CITE-seq实验的分析能力.
- 促进了涉及细胞表面蛋白质的分子机制的研究.
相关概念视频
01:58Cell Specific Gene Expression
13.6K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
13.6K
01:05Two-Compartment Open Model: Overview
160
Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...
The...
160
01:15Drug Concentration Versus Time Correlation
832
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
832