Artificial neural network system for cell classification using single cell RNA expression

Lin, Xin, Zhong, Jiahui, Lyu, Minjie, Lin, Sen, Keskin, Derin B., Zhang, Guanglan, Brusic, Vladimir and Chitkushev, Lou T. (2021) Artificial neural network system for cell classification using single cell RNA expression. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 16-19 Dec. 2020, Seoul, Korea (South).

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We implemented an automated system for single-cell classification using artificial neural networks (ANN). Our system takes single-cell gene expression sparse matrices and trains ANN to classify cell types and subtypes. The assemblies of ANNs predict cell classes by voting. We tested the system in a case study where we trained ANNs with a dataset containing approximately 120,000 single cells and tested the resulting model using an independent data set of 13,000 single cells. The overall accuracy of the 5-class classification was 95%. We trained and tested a total of 100 ANNs in 10 cycles. The prediction system demonstrated excellent reproducibility. The analysis of misclassifications indicated that 2% were likely classification errors, while the remaining 3% were likely due to mislabeled types and subtypes in the test set.

Item Type: Conference or Workshop Item (Paper)
Keywords: ANN, automation of cell classification, gene expression, PBMC, prediction system, supervised machine learning
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number:
Depositing User: Wang, Danni
Date Deposited: 10 Mar 2021 06:26
Last Modified: 10 Mar 2021 06:26

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