PBMC Cell Classification from Single Cell mRNA Expression by Artificial Neural

We performed classification of healthy Peripheral Blood Mononuclear Cells cell types using four methods Artificial Neural Network (ANN), Profiles, Protein Markers (PMs), and RNA markers (RNAMs). Profiles represent patterns of gene expressions characteristic of the subtypes of cells. PMs are protein found exclusively in certain types or subtypes of cells, or represent particular cell states, RNAMs are genes which demonstrate significant differential expressions between cell types. A total of 109 datasets from four different sources containing $\sim$ 120,000 single cells gene expression were used to train and test prediction models. We combined the methods which perform prediction using the whole set of gene features (ANN and Profiles), and those that used specific gene features (PMs and RNAMs) to predict the cell type. The overall classification accuracy was 94.8% for ANN, 94.5% for Profiles, 90.7% for PMs, 67.9% for RNAMs. The combination of four methods showed accuracy of 90.9% with high confidence of positive predictions. The combination of four methods allowed identification of mislabeled cell types in test data sets.