Prediction of PBMC Cell Types Using scRNAseq Reference Profiles

Abstract

Single cell transcriptomics enables a high-resolution concurrent measurement of gene expression from tens of thousands of cells. We developed a method for determining standardized profiles from SCT data. We defined 48 data sets from 13 different studies and developed single-cellderived-class' (SCDC) profiles representing multiple classes and subclasses of peripheral blood mononuclear cells (PBMC). We applied pattern recognition analysis by calculating the distance from each query cell to the SCDC profiles (excluding the profiles of the query cells). Classification of cells by pattern recognition showed excellent performance for PBMC that were isolated, but not further processed by cell sorting.