{"doi":"10.1016/j.xpro.2025.103608","title":"Protocol to boost the robustness and accuracy of spatial transcriptomics algorithms using ensemble techniques","abstract":"Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix. We then detail procedures for using the similarity matrix to identify spatial domains and obtain new embeddings. For complete details on the use and execution of this protocol, please refer to Cai et al. 1 • Improved workflows for spatial domain detection • Procedures for using ensemble learning to boost existing methods • Robust performance with high generality • Open-source Python code with specific comments and tutorials Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix. We then detail procedures for using the similarity matrix to identify spatial domains and obtain new embeddings.","journal":"STAR Protocols","year":2025,"id":540208,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9488,"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":1265946,"name":"Shushan Wu","orcid":"0000-0001-7594-0273","position":1,"is_corresponding":false},{"id":1265945,"name":"Huimin Cheng","orcid":"0000-0001-7992-8175","position":2,"is_corresponding":false},{"id":331412,"name":"Wenxuan Zhong","orcid":"0000-0001-9006-622X","position":3,"is_corresponding":false},{"id":31866,"name":"Guo‐Cheng Yuan","orcid":"0000-0002-2283-4714","position":4,"is_corresponding":false},{"id":548645,"name":"Ping Ma","orcid":"0000-0002-2446-3849","position":5,"is_corresponding":false},{"id":1265944,"name":"Jiazhang Cai","orcid":"0009-0002-0267-9726","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-19T02:52:38.861025Z","pmid":"39879360","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":[]}