TREG

TREG implements a data-driven method for identifying candidate Total RNA Expression Genes (TREGs) from single nucleus RNA-seq (snRNA-seq) data to facilitate estimation of total RNA abundance using single molecule fluorescent in situ hybridization (smFISH).

Key Features:

• Expression invariance measure: Uses a measure of expression invariance to rank and identify candidate TREGs across heterogeneous cell types with different sizes and transcriptional activity levels.
• Single nucleus RNA-seq compatibility: Processes snRNA-seq data from postmortem human brain tissue to find candidate TREGs.
• Validation against housekeeping genes: Enables comparison of identified candidate TREGs against classic housekeeping genes.

Scientific Applications:

• RNA-seq deconvolution improvement: Provides RNA abundance and cell size parameters to improve RNA-seq deconvolution algorithms for more accurate estimation of cell type proportions.
• Total RNA content estimation via smFISH: Identifies TREGs that correlate with total RNA abundance for use in single molecule fluorescent in situ hybridization experiments.
• Postmortem human brain research: Supports identification of region-specific TREGs such as AKT3, a top TREG across five brain regions including the dorsolateral prefrontal cortex.

Methodology:

The method computes a measure of expression invariance across heterogeneous cell types from single cell RNA-sequencing data to rank and identify candidate Total RNA Expression Genes.