ArticleJournal of proteome research2025
Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.
Article in Journal of proteome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multimodal analysis of molecular remodeling in aging spleen identified global and cell type specific changes.bioRxiv : the preprint server for biology · 2026Article
- Multiomic profiling of ER-positive HER2-negative breast cancer reveals markers associated with metastatic spread.Breast cancer research : BCR · 2026Observational
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Authors and funding
6 authors.
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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.
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