Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation.
Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation.
复制标题
DOI：
10.1093/bioinformatics/btad360
复制
发表时间：
2023-06-01
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
按细胞类型注释的单细胞RNA测序（scRNA-seq）数据可用于各种下游生物学应用，例如在单细胞水平上分析基因表达。然而，手动分配这些注释与已知的标记基因是既耗时又主观的。我们提出了一种基于图卷积网络（GCN）的方法来自动化注释过程。我们的过程建立在现有的标记方法的基础上，使用最先进的工具通过共识找到具有高度置信标签分配的细胞，并使用半监督GCN传播这些置信标签。使用模拟数据和来自不同组织的两个scRNA-seq数据集，我们证明了我们的方法比简单的共识算法和基础工具的平均值提高了准确性。我们还比较了我们的方法的非参数邻居多数的方法，显示出可比的结果。然后，我们证明了我们的GCN方法允许特征解释，识别细胞类型分类的重要基因。我们展示了用PyTorch编写的完整管道，作为自动化和解释scRNA-seq数据分类的端到端工具。我们在本文中进行实验和使用我们的模型的代码可以在https://github.com/lewinsohndp/scSHARP上获得。
Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP.
登录
查看更多内容
影响因子：
6
作者：
Pasquini G;Rojo Arias JE;Schäfer P;Busskamp V
通讯作者：
Busskamp V
影响因子：
11.8
作者：
Green CD;Ma Q;Manske GL;Shami AN;Zheng X;Marini S;Moritz L;Sultan C;Gurczynski SJ;Moore BB;Tallquist MD;Li JZ;Hammoud SS
通讯作者：
Hammoud SS
影响因子：
6.2
作者：
Wang, Yue;Sun, Yongbin;Solomon, Justin M.
通讯作者：
Solomon, Justin M.
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed
影响因子：
12.3
作者：
Guo H;Li J
通讯作者：
Li J