Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types
Genome Biology(2022)
摘要
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
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要