Paper Info
Title
Single-Cell Classification Using Graph Convolutional Networks
Abstract
Background Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. Results In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. Conclusions Results indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
Year
DOI
Venue
2021
10.1186/s12859-021-04278-2
BMC BIOINFORMATICS
Keywords
DocType
Volume
Single cell RNA sequencing, Cell classification, Deep learning, Graph convolutional neural network, Convolutional neural network
Journal
22
Issue
ISSN
Citations 
1
1471-2105
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Tianyu Wang112030.07
Jun Bai200.34
Sheida Nabavi3188.68