Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types

Next-generation sequencing technologies have facilitated data-driven identification of gene sets with different features including genes with stable expression, cell-type specific expression, or spatially variable expression. Here, we aimed to define and identify a new class of “control” genes called Total RNA Expression Genes (TREGs), which correlate with total RNA abundance in heterogeneous cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single cell RNA-sequencing (RNA-seq) data, available as an R/Bioconductor package at . We demonstrated the utility of our method in the postmortem human brain using multiplex single molecule fluorescent in situ hybridization (smFISH) and compared candidate TREGs against classic housekeeping genes. We identified AKT3 as a top TREG across five brain regions, especially in the dorsolateral prefrontal cortex. ### Competing Interest Statement The authors have declared no competing interest. * AMY : amygdala Astro : astrocytes DLPFC : dorsolateral prefrontal cortex Excit : excitatory neurons Expression Rank : rank of the log normalized counts expression values for a given gene and nucleus, with high expression values translating into high rank values HPC : hippocampus HK : housekeeping Inhib : inhibitory neurons Micro : microglia NAc : nucleus accumbens Oligo : oligodendrocytes OPC : oligodendrocyte progenitor cells Proportion Zero : defined in Methods: Expression and Proportion Zero filtering RI : Rank Invariance sACC : subgenual anterior cingulate cortex smFISH : single-molecule fluorescent in situ hybridization TREG : total RNA expression gene