Poster Presentation 43rd Lorne Genome Conference 2022

Comprehensive evaluation of deconvolution methods for human brain gene expression (#258)

Gavin J Sutton 1 , Daniel Poppe 2 3 , Rebecca K Simmons 2 3 , Kieran Walsh 1 , Urwah Nawaz 4 , Ryan Lister 2 3 , Johann A Gagnon-Bartsch 5 , Irina Voineagu 1
  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
  2. Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, Western Australia, Australia
  3. Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia,, Nedlands, Western Australia, Australia
  4. Adelaide Medical School, Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia
  5. Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

Transcriptome deconvolution aims to estimate the cellular composition of a 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 sub-types 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 plus 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 2,000 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.