Artificial neural network system for cell classification using single cell RNA expressionTools 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).
Official URL: http://dx.doi.org/10.1109/BIBM49941.2020.9313498
AbstractWe 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.
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