TY - JOUR
T1 - Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data
AU - Andrade Barbosa, B. rbara
AU - van Asten, Saskia D.
AU - Oh, Ji Won
AU - Farina-Sarasqueta, Arantza
AU - Verheij, Joanne
AU - Dijk, Frederike
AU - van Laarhoven, Hanneke W. M.
AU - Ylstra, Bauke
AU - Garcia Vallejo, Juan J.
AU - van de Wiel, Mark A.
AU - Kim, Yongsoo
N1 - Funding Information: The authors thank Kai Ruan for his careful review of the derivation of the BLADE algorithm. This project was supported by stichting Cancer Center Amsterdam (CCA2019-9-62). Publisher Copyright: © 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.
AB - Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.
UR - http://www.scopus.com/inward/record.url?scp=85117700346&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41467-021-26328-2
DO - https://doi.org/10.1038/s41467-021-26328-2
M3 - Article
C2 - 34671028
SN - 2041-1723
VL - 12
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 6106
ER -