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Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM by Nicholas K. O’Neill; Thor D. Stein; Junming Hu; Habbiburr Rehman; Joshua D. Campbell; Masanao Yajima; Xiaoling Zhang; Lindsay A. Farrer is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM about?

## 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 accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. ## Conclusion DeTREM improve

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

Author

Nicholas K. O’Neill; Thor D. Stein; Junming Hu; Habbiburr Rehman; Joshua D. Campbell; Masanao Yajima; Xiaoling Zhang; Lindsay A. Farrer

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)