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granulator: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data ================ <!-- badges: start --> [![Lifecycle:stable](]( [![license](]( <!-- badges: end --> ## 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**. <table> <colgroup> <col style="width: 11%" /> <col style="width: 8%" /> <col style="width: 27%" /> <col style="width: 35%" /> <col style="width: 16%" /> </colgroup> <thead> <tr class="header"> <th>Name</th> <th>Function</th> <th>Method</th> <th>License</th> <th>Reference</th> </tr> </thead> <tbody> <tr class="odd"> <td>ols</td> <td>stats::lsfit</td> <td>Ordinary least squares</td> <td>free (<a href="">GPL-2</a>)</td> <td></td> </tr> <tr class="even"> <td>nnls</td> <td>nnls::nnls</td> <td>Non-negative least squares</td> <td>free (<a href="">GPL-2, GPL-3</a>)</td> <td>reimplemented based on <span class="citation" data-cites="Abbas2009">(Abbas et al. 2009)</span></td> </tr> <tr class="odd"> <td>qprogwc</td> <td>limSolve::lsei</td> <td>Quadratic programming with non-negativity and sum-to-one constraint</td> <td>free (<a href="">GPL-2, GPL-3</a>)</td> <td>reimplemented based on <span class="citation" data-cites="Gong2013">(Gong and Szustakowski 2013)</span></td> </tr> <tr class="even"> <td>qprog</td> <td>limSolve::Solve</td> <td>Quadratic programming without constraints</td> <td>free (<a href="">GPL-2, GPL-3</a>)</td> <td></td> </tr> <tr class="odd"> <td>rls</td> <td>MASS::rlm</td> <td>Re-weighted least squares</td> <td>free (<a href="">GPL-2, GPL-3</a>)</td> <td>reimplemented based on <span class="citation" data-cites="Monaco2019">(Monaco et al. 2019)</span></td> </tr> <tr class="even"> <td>svr</td> <td>e1071::svr</td> <td>Support vector regression</td> <td>free (<a href="">GPL-2, GPL-3</a>)</td> <td>reimplemented based on <span class="citation" data-cites="Newman2015">(Newman et al. 2015)</span></td> </tr> <tr class="odd"> <td>dtangle</td> <td>dtangle::dtangle</td> <td>Linear mixing model</td> <td>free (<a href="">GPL-3</a>)</td> <td><span class="citation" data-cites="Hunt2018">(Hunt et al. 2018)</span></td> </tr> </tbody> </table> **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. “<span class="nocase">Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus</span>.” *PLoS ONE* 4 (7). <>. 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</span>.” *Bioinformatics* 29 (8): 1083–85. <>. 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. <>. 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</span>.” *Cell Reports* 26 (6): 1627–1640.e7. <>. 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. “<span class="nocase">Robust enumeration of cell subsets from tissue expression profiles</span>.” *Nature Methods* 12 (5): 453–57. <>.