Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Arthur Ko, Kristina M. Garske, Jae Hoon Sul, Kirsi H. Pietiläinen, Päivi Pajukanta () and Eran Halperin ()
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Brandon Jew: UCLA
Marcus Alvarez: David Geffen School of Medicine at UCLA
Elior Rahmani: UCLA
Zong Miao: UCLA
Arthur Ko: David Geffen School of Medicine at UCLA
Kristina M. Garske: David Geffen School of Medicine at UCLA
Jae Hoon Sul: UCLA
Kirsi H. Pietiläinen: University of Helsinki
Päivi Pajukanta: UCLA
Eran Halperin: David Geffen School of Medicine at UCLA

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

Date: 2020
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DOI: 10.1038/s41467-020-15816-6

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