{"doi":"10.1093/bioinformatics/btaa153","title":"MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples","abstract":"MOTIVATION: Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance. RESULTS: We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data. AVAILABILITY AND IMPLEMENTATION: R code available at https://github.com/S-YIN/MIXnorm. CONTACT: swang@smu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2020,"id":89803,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9472,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":426336,"name":"Xinlei Wang","orcid":"0000-0002-8561-6511","position":1,"is_corresponding":false},{"id":453393,"name":"Gaoxiang Jia","orcid":"0000-0001-7242-4767","position":2,"is_corresponding":false},{"id":326811,"name":"Yang Xie","orcid":"0000-0001-9456-1762","position":3,"is_corresponding":false},{"id":454162,"name":"Shen Yin","orcid":null,"position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-18T22:02:27.195966Z","pmid":"32134470","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":[]}