TY - GEN
T1 - Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification
AU - Gao, Jing
AU - Zhang, Fa
AU - Hu, Kai
AU - Cui, Xuefeng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs).
AB - Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs).
KW - Convolutional neural network
KW - Graph neural network
KW - Spatial domain identification
KW - Spatial transcriptomics
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85146654481&partnerID=8YFLogxK
U2 - 10.1109/BIBM55620.2022.9995701
DO - 10.1109/BIBM55620.2022.9995701
M3 - Conference contribution
AN - SCOPUS:85146654481
T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
SP - 200
EP - 205
BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
A2 - Adjeroh, Donald
A2 - Long, Qi
A2 - Shi, Xinghua
A2 - Guo, Fei
A2 - Hu, Xiaohua
A2 - Aluru, Srinivas
A2 - Narasimhan, Giri
A2 - Wang, Jianxin
A2 - Kang, Mingon
A2 - Mondal, Ananda M.
A2 - Liu, Jin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Y2 - 6 December 2022 through 8 December 2022
ER -
