{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li M"],"funding":["NCI NIH HHS","The Hope Foundation"],"pagination":["btae431"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11246167"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["40(7)"],"pubmed_abstract":["<h4>Motivation</h4>T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed.<h4>Results</h4>In this paper, we develop an innovative \"multi-bin\" rarefying approach that partitions samples into multiple bins according to their library sizes, conduct"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data."],"pmcid":["PMC11246167"],"funding_grant_id":["U10 CA180819","U10CA180819"],"pubmed_authors":["Hua X","Zhao N","Li S","Wu MC","Li M"],"additional_accession":[]},"is_claimable":false,"name":"A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data.","description":"<h4>Motivation</h4>T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed.<h4>Results</h4>In this paper, we develop an innovative \"multi-bin\" rarefying approach that partitions samples into multiple bins according to their library sizes, conduct","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-03T05:34:01.65Z","creation":"2026-04-24T03:14:33.72Z"},"accession":"S-EPMC11246167","cross_references":{"pubmed":["38950175"],"doi":["10.1093/bioinformatics/btae431"]}}