ArticleiScience2024
ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- Deconvolution Methods to Link Multi-Omics Data to Cell Type-Specific Extracellular Vesicle Abundances.Proteomics · 2026Review
- Cracking the resistance code: The molecular reclassification of cancer and precision therapy strategies.Seminars in cancer biology · 2025Review
- Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve.iScience · 2025Article
- Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.Journal of proteome research · 2025Article
- Quantitative proteomics analysis of triple-negative breast cancers.NPJ precision oncology · 2025Article
- Histology-resolved proteomics reveals distinct tumor and stromal profiles in low- and high-grade prostate cancer.Clinical proteomics · 2025Article
- Real-time functional proteomics enhances therapeutic targeting in precision oncology molecular tumor boards.NPJ precision oncology · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.Clinical proteomics · 2024Article
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 Consensus
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.