ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Pang ning Teng, Joshua P. Schaaf, Tamara Abulez, Brian L. Hood, Katlin N. Wilson, Tracy J. Litzi, David Mitchell, Kelly A. Conrads, Allison L. Hunt, Victoria Olowu, Julie Oliver, Fred S. Park, Marshé Edwards, Ai Chun Chiang, Matthew D. Wilkerson, Praveen Kumar Raj-Kumar, Christopher M. Tarney, Kathleen M. Darcy, Neil T. Phippen, G. Larry MaxwellThomas P. Conrads*, Nicholas W. Bateman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at

Original languageEnglish
Article number109198
Issue number3
StatePublished - 15 Mar 2024
Externally publishedYes


  • Computational bioinformatics
  • Proteomics
  • Transcriptomics


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