{"doi":"10.1021/acs.jproteome.5c00980","title":"Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors","abstract":"Understanding intratumoral heterogeneity is essential for elucidating tumor biology. Compared to RNA expression, omics-level characterization of cell-type-specific protein expression remains a technical challenge. Bulk mass spectrometry (MS) provides abundant proteomics resources to infer cell-type specificity via data deconvolution; however, it is unclear which proteomic quantification formats are optimal, as they differ from the data types for which most deconvolution methods were designed. Here, leveraging recently generated large-cohort proteogenomics data, we systematically evaluated different MS proteomics quantification formats and preprocessing strategies to resolve cell-type-specific protein expression. Our results indicate that while label-free spectral counts can be used directly, TMT MS1 intensities and MS2 ratios are less suitable and require appropriate data transformation. We demonstrate that a 'min-score' transformation significantly improves MS1 intensity-based deconvolution, providing useful insights for subtyping pancreatic cancer. Moreover, we identified the coefficient of variation (CV) as a robust statistical indicator of deconvolution suitability. Finally, we developed \"ProTransDeconv\", an R package integrating data transformation, deconvolution, and quality checks for major MS proteomics data formats. This work provides practical guidance for deconvolving bulk proteomics to study cell-type-specific protein-level dysregulation.","journal":"Journal of Proteome Research","year":2025,"id":537394,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9189,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1423198,"name":"Qi Zhou","orcid":"0000-0002-5697-2998","position":1,"is_corresponding":false},{"id":225689,"name":"Chen Huang","orcid":"0000-0003-2777-7365","position":2,"is_corresponding":false},{"id":762293,"name":"Yingnan Song","orcid":"0009-0003-2929-0638","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:12.997494Z","pmid":"41328725","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}