当前位置: X-MOL 学术RNA › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data
RNA ( IF 4.2 ) Pub Date : 2020-06-12 , DOI: 10.1261/rna.074427.119
Suhas Srinivasan , Anastasia Leshchyk , Nathan T. Johnson , Dmitry Korkin

Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to single-cell transcriptomics dataset obtained from a triple-negative breast cancer tumor to identify potential cancer subclones accentuated by copy-number variation and investigate the role of clonal heterogeneity. Our method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

中文翻译:

使用单细胞 RNA-seq 数据进行稳健细胞类型分析的混合深度聚类方法

单细胞 RNA 测序 (scRNA-seq) 是一项最新技术,可以对细胞亚型和特定细胞状态进行细粒度发现。它通常使用机器学习方法,例如特征学习、聚类和分类,来帮助从 scRNA-seq 数据中发现新信息。然而,当前的方法不太适合处理由实验产生的大量噪声或由于相同类型细胞的差异而发生的变化。在这里,我们开发了一种新的混合方法,即深度无监督单细胞聚类 (DUSC),它将基于深度学习架构的特征生成与基于模型的聚类算法相结合,以找到单细胞转录组的紧凑且信息丰富的表示数据生成强大的集群。我们还包括一种技术来估计深度学习模型中有效数量的潜在特征。我们的方法优于经典和最先进的特征学习和聚类方法,接近监督学习的准确性。我们将 DUSC 应用于从三阴性乳腺癌肿瘤获得的单细胞转录组学数据集,以识别因拷贝数变异而加剧的潜在癌症亚克隆,并研究克隆异质性的作用。我们的方法对社区免费提供,并有望促进我们对生物体细胞图谱的理解,并提供改善患者诊断和治疗的方法。我们的方法优于经典和最先进的特征学习和聚类方法,接近监督学习的准确性。我们将 DUSC 应用于从三阴性乳腺癌肿瘤获得的单细胞转录组学数据集,以识别因拷贝数变异而加剧的潜在癌症亚克隆,并研究克隆异质性的作用。我们的方法对社区免费提供,并有望促进我们对生物体细胞图谱的理解,并提供改善患者诊断和治疗的方法。我们的方法优于经典和最先进的特征学习和聚类方法,接近监督学习的准确性。我们将 DUSC 应用于从三阴性乳腺癌肿瘤获得的单细胞转录组学数据集,以识别因拷贝数变异而加剧的潜在癌症亚克隆,并研究克隆异质性的作用。我们的方法对社区免费提供,并有望促进我们对生物体细胞图谱的理解,并提供改善患者诊断和治疗的方法。我们将 DUSC 应用于从三阴性乳腺癌肿瘤获得的单细胞转录组学数据集,以识别因拷贝数变异而加剧的潜在癌症亚克隆,并研究克隆异质性的作用。我们的方法对社区免费提供,并有望促进我们对生物体细胞图谱的理解,并提供改善患者诊断和治疗的方法。我们将 DUSC 应用于从三阴性乳腺癌肿瘤获得的单细胞转录组学数据集,以识别因拷贝数变异而加剧的潜在癌症亚克隆,并研究克隆异质性的作用。我们的方法对社区免费提供,并有望促进我们对生物体细胞图谱的理解,并提供改善患者诊断和治疗的方法。
更新日期:2020-06-12
down
wechat
bug