Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data

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.