Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.

ABSTRACT:

Background

Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies.

Results

We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accur

SUBMITTER: O'Neill NK 

PROVIDER: S-EPMC10507917 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature