Comprehensive evaluation of deconvolution methods for human brain gene expression

Gavin J Sutton, Daniel Poppe, Rebecca K Simmons, Kieran Walsh, Urwah Nawaz, Ryan Lister, Johann A Gagnon-Bartsch, Irina Voineagu

Research output: Contribution to journalArticlepeer-review

25 Citations (Web of Science)

Abstract

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

Original languageEnglish
Article number1358
JournalNature Communications
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2022

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