ArticleBMC bioinformatics2022
TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.Science advances · 2026Article
- Learning predictive signatures of HLA type from T-cell repertoires.PLoS computational biology · 2025Article
- TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.Frontiers in genetics · 2024Article
- Counting is almost all you need.Frontiers in immunology · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundT cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed.
methodsWe introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined.
resultsSimulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients.
conclusionThe TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.
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Registered trials
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