{"doi":"10.1101/2021.09.14.21263182","title":"FastMix: A Versatile Multi-Omics Data Integration Pipeline for Cell Type-Specific Biomarker Inference","abstract":"Abstract We developed a novel analytic pipeline - FastMix - to integrate flow cytometry, bulk transcriptomics, and clinical covariates for statistical inference of cell type-specific gene expression signatures. FastMix addresses the “large p , small n ” problem via a carefully designed linear mixed effects model (LMER), which is applicable for both cross-sectional and longitudinal studies. With a novel moment-based estimator, FastMix runs and converges much faster than competing methods for big data analytics. The pipeline also includes a cutting-edge flow cytometry data analysis method for identifying cell population proportions. Simulation studies showed that FastMix produced smaller type I/II errors with more accurate parameter estimation than competing methods. When applied to real transcriptomics and flow cytometry data in two vaccine studies, FastMix -identified cell type-specific signatures were largely consistent with those obtained from the single cell RNA-seq data, with some unique interesting findings.","journal":"medRxiv","year":2021,"id":226841,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9476,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":830190,"name":"Hao Sun","orcid":"0000-0002-6652-4101","position":1,"is_corresponding":false},{"id":530940,"name":"Aishwarya Mandava","orcid":null,"position":2,"is_corresponding":false},{"id":38324,"name":"Brian D. Aevermann","orcid":"0000-0003-1346-1327","position":3,"is_corresponding":false},{"id":258306,"name":"Tobias R. Kollmann","orcid":"0000-0003-2403-9762","position":4,"is_corresponding":false},{"id":6389,"name":"Richard H. Scheuermann","orcid":"0000-0003-1355-892X","position":5,"is_corresponding":false},{"id":368417,"name":"Xing Qiu","orcid":"0000-0002-2330-3544","position":6,"is_corresponding":false},{"id":1660,"name":"Qian Yu","orcid":"0000-0002-6224-5607","position":7,"is_corresponding":false},{"id":38372,"name":"Renee Zhang","orcid":"0000-0003-2707-5881","position":0,"is_corresponding":true}],"reference_count":82,"raw_metadata":null,"created_at":"2026-07-18T23:54:38.004707Z","pmid":null,"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":[]}