granulator: Rapid benchmarking of methods for in silico deconvolution of bulk RNA-seq data

Introduction

Heterogeneity in the cellular composition of bulk RNA-seq data may prevent or bias the results from differential expression analysis. To circumvent this limitation, in silico deconvolution infers cell type abundances by modelling gene expression levels as weighted sums of the cell-type specific expression profiles. Several computational methods have been developed to estimate cell type proportions from bulk transcriptomics data, and to account for cell type heterogeneity in the statistical analysis. The R package granulator provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. We demonstrate its usage on published bulk RNA-seq data from peripheral blood mononuclear cells.

Methods

The methods currently implemented in granulator are reported in Table 1.

Name	Function	Method	License	Reference
ols	stats::lsfit	Ordinary least squares	free (GPL-2)
nnls	nnls::nnls	Non-negative least squares	free (GPL-2, GPL-3)	reimplemented based on (Abbas et al. 2009)
qprogwc	limSolve::lsei	Quadratic programming with non-negativity and sum-to-one constraint	free (GPL-2, GPL-3)	reimplemented based on (Gong and Szustakowski 2013)
qprog	limSolve::Solve	Quadratic programming without constraints	free (GPL-2, GPL-3)
rls	MASS::rlm	Re-weighted least squares	free (GPL-2, GPL-3)	reimplemented based on (Monaco et al. 2019)
svr	e1071::svr	Support vector regression	free (GPL-2, GPL-3)	reimplemented based on (Newman et al. 2015)
dtangle	dtangle::dtangle	Linear mixing model	free (GPL-3)	(Hunt et al. 2018)

Table 1 - Deconvolution methods. List of deconvolution algorithms available in granulator.

Installation

granulator can be installed from Bioconductor using:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("granulator")

The package can be loaded using:

library(granulator)

Data

The datasets included in the package comprises bulk RNA-seq gene expression data of peripheral blood mononuclear cells (PBMCs) from 12 healthy donors and bulk RNA-seq data of 29 isolated immune cell types from 4 healthy donors (Monaco et al. 2019), publicly available at NCBI database under GEO accession number GSE107011.

Vignettes

We show how to use granulator for the deconvolution of bulk RNA-seq data from peripheral blood mononuclear cells (PBMCs) into the individual cellular components and how to assess the quality of the obtained predictions in the following vignette: Deconvolution of bulk RNA-seq data with granulator.

References

Abbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee, Zora Modrusan, and Hilary F. Clark. 2009. “Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.” PLoS ONE 4 (7). https://doi.org/10.1371/journal.pone.0006098.

Gong, Ting, and Joseph D Szustakowski. 2013. “<span class="nocase">DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.” Bioinformatics 29 (8): 1083–85. https://doi.org/10.1093/bioinformatics/btt090.

Hunt, Gregory J, Saskia Freytag, Melanie Bahlo, and Johann A Gagnon-Bartsch. 2018. “Dtangle: Accurate and Robust Cell Type Deconvolution.” Bioinformatics 35 (12): 2093–99. https://doi.org/10.1093/bioinformatics/bty926.

Monaco, Gianni, Bernett Lee, Weili Xu, Seri Mustafah, You Yi Hwang, Christophe Carré, Nicolas Burdin, et al. 2019. “<span class="nocase">RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.” Cell Reports 26 (6): 1627–1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041.

Newman, Aaron M., Chih Long Liu, Michael R. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, Maximilian Diehn, and Ash A. Alizadeh. 2015. “Robust enumeration of cell subsets from tissue expression profiles.” Nature Methods 12 (5): 453–57. https://doi.org/10.1038/nmeth.3337.

Novartis / granulator

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