{"doi":"10.1155/2013/610297","title":"Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies","abstract":"<jats:p>Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations and gene oncology examples. The bootstrap single-step min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure and the bootstrap step-down min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation max<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\"><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:math>procedure and the permutation min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.</jats:p>","journal":"Computational and Mathematical Methods in Medicine","year":2013,"id":590077,"datarank":0.8323051808604902,"base_score":2.639057329615259,"endowment":2.639057329615259,"self_citation_contribution":0.3958585994422889,"citation_network_contribution":0.4364465814182013,"self_endowment_contribution":0.3958585994422889,"citer_contribution":0.4364465814182013,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":12,"citers_with_citation_signal":11,"citers_with_endowment":11,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1509813,"name":"Timothy D. 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The bootstrap single-step min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure and the bootstrap step-down min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation max<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\"><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:math>procedure and the permutation min<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\"><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:math>procedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.</jats:p>","is_dataset_classified":null,"base_score":2.639057329615259,"endowment":2.639057329615259,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24348741","pmcid":"PMC3853148","openalex_id":"https://openalex.org/W1985918072","authors":[],"funders":[{"funder_name":"National Institute of Minority Health and Health Disparities","grant_id":"U54MD007584","title":null},{"funder_name":"National Institute of Minority Health and Health Disparities","grant_id":"G12MD007601","title":null},{"funder_name":"National Institutes of Health","grant_id":"3G12MD007601-30S1","title":"Bioscience Research Infrastructure Development for Grant Enhancement and Success"},{"funder_name":"National Institutes of Health","grant_id":"5U54MD007584-06","title":"RCMI Multidisciplinary And Translational Research Infrastructure EXpansion (RAMAT"},{"funder_name":"National Institutes of Health","grant_id":"5U54MD007584-07","title":"RCMI Multidisciplinary And Translational Research Infrastructure EXpansion (RAMAT"}],"total_grants":5,"fwci":0.1376,"citation_percentile":0.52760981,"influential_citations":0,"citation_trend":[{"year":2015,"count":1},{"year":2017,"count":2},{"year":2018,"count":2},{"year":2019,"count":5},{"year":2020,"count":2},{"year":2023,"count":1}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://downloads.hindawi.com/journals/cmmm/2013/610297.pdf","host_type":"journal"},{"url":"https://downloads.hindawi.com/journals/cmmm/2013/610297.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cmmm/2013/610297.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cmmm/2013/610297.xml","host_type":"publisher"},{"url":"https://doi.org/10.1155/2013/610297","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/24348741","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3853148","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC3853148","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC3853148?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1155/2013/610297","host_type":""},{"url":"https://zbmath.org/6415009","host_type":""},{"url":"https://dx.doi.org/10.1155/2013/610297","host_type":""}],"fields_of_study":["Gene expression and cancer classification","Molecular Biology Techniques and Applications","Genomics and Chromatin Dynamics","0301 basic medicine","0303 health sciences","03 medical and health sciences","Algorithms","Computer Simulation","Female","Gene Expression Profiling","Gene Expression Regulation, Neoplastic","Genome-Wide Association Study","Humans","Medical Oncology","Models, Theoretical","Neoplasms","Oligonucleotide Array Sequence Analysis","Ovarian Neoplasms","Research Design","Sample Size"],"mesh_terms":["Algorithms","Computer Simulation","Female","Humans","Medical Oncology","Models, Theoretical","Neoplasms","Ovarian Neoplasms","Research Design","Gene Expression Regulation, Neoplastic","Sample Size","Oligonucleotide Array Sequence Analysis","Gene Expression Profiling","Genome-Wide Association Study"],"keywords":["Resampling","Stability (learning theory)","Computational biology","Computer science","Oncology","Precision oncology","Medical physics","Medicine","Internal medicine","Biology","Machine learning","Cancer","Artificial intelligence","Ovarian Neoplasms","General biostatistics","Gene Expression Profiling","Models, Theoretical","Medical Oncology","Gene Expression Regulation, Neoplastic","Medical applications (general)","Research Design","Neoplasms","Sample Size","Humans","Computer Simulation","Female","Algorithms","Research Article","Genome-Wide Association Study","Oligonucleotide Array Sequence Analysis"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"geo"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-24T12:56:26.215664Z","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":[]}