Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

TLDR: In this article, the authors used artificial neural networks (ANN) to assess the ability of ANN to classify main blood mononuclear cells (PBMC) cell types, and the overall prediction accuracy reached 93% in 4-class classification.

Abstract: Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.