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.
|Publication status||Published Online - 13 Jan 2021|
|Event||2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Seoul, Korea (South)|
Duration: 16 Dec 2020 → 19 Dec 2020
|Conference||2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)|
|Period||16/12/20 → 19/12/20|
- ANN, automation of cell classification, gene expression, PBMC, prediction system, supervised machine learning