Abstract
Inferring the cell-type composition of bulk samples can provide biological insight. While bulk transcriptomics data has been extensively used for this purpose, the use of proteomics data has remained unexplored until recently. This study evaluates computational approaches for estimating immune cell composition using bulk sample proteomics data. Leveraging defined immune cell populations and simulated mixtures, we assess the impact of preprocessing methods and software tools on cell deconvolution outcomes. Our findings demonstrate the feasibility of using proteomics data for cell-type deconvolution, with Pearson correlations for estimated proportions in simulated sample mixtures above 0.9 when employing optimal missing value imputation and reference matrix generation parameters. We further provide an R package, proteoDeconv, to facilitate the preprocessing of proteomics data for deconvolution and parsing of results. This study highlights the feasibility of using proteomics for analyzing cell-type composition in biological samples.