Knowledge base
Integrating both TCR and GEX information
ConGA [Schattgen et al., 2021] (Clonotype Neighbor Graph Analysis) is a computational method that integrates TCR and GEX data to identify TCRs that are associated with a specific phenotype. ConGA uses a graph theoretic approach that identifies correlations between GEX profile and TCR sequence through statistical analysis of GEX and TCR similarity graphs.
Tessa [Zhang et al., 2021] is a Bayesian method that integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells.
mvTCR [Drost et al., 2024] is a computational method that integrates TCR and GEX data to identify TCRs that are associated with a specific phenotype. mvTCR uses a multi-view learning approach to model the relationship between TCRs and GEX profiles. The method learns a joint representation of TCRs and GEX profiles that captures the relationship between the two data modalities. mvTCR then uses the joint representation to predict the phenotype of new TCRs based on their GEX profiles.
scNAT: [Lai et al., 2024]
MIST [Zhu et al., 2023]
Clustering and similarity search for TCRs
GLIPH [Glanville et al., 2017]
GLIPH2 [Huang et al., 2020]
GIANA [Zhang et al., 2021]
TCRdist1/2 [Dash et al., 2017]
TCRDist3: [Mayer-Blackwell et al., 2022]
clusTCR [Valkiers et al., 2021]
Antigen specificity prediction for TCRs
Methods that consider the structure of the TCR-peptide-MHC complex:
PISTE [Feng et al., 2024]
EPACT [Zhang et al., 2024]
TEIM-res [Peng et al., 2023]
DeepAIR [Zhao et al., 2023]
Sequence-level methods:
TEIM-Seq [Peng et al., 2023]
NetTCR-2.0 [Montemurro et al., 2021]
PanPep: [Gao et al., 2023]
DeepTCR [Sidhom et al., 2021]
ERGO [Springer et al., 2020]
TCR-BERT [Wu et al., 2024]
References
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Felix Drost, Yang An, Irene Bonafonte-Pardàs, Lisa M. Dratva, Rik G. H. Lindeboom, Muzlifah Haniffa, Sarah A. Teichmann, Fabian Theis, Mohammad Lotfollahi, and Benjamin Schubert. Multi-modal generative modeling for joint analysis of single-cell t cell receptor and gene expression data. Nature Communications, July 2024. URL: http://dx.doi.org/10.1038/s41467-024-49806-9, doi:10.1038/s41467-024-49806-9.
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Huang Huang, Chunlin Wang, Florian Rubelt, Thomas J. Scriba, and Mark M. Davis. Analyzing the mycobacterium tuberculosis immune response by t-cell receptor clustering with gliph2 and genome-wide antigen screening. Nature Biotechnology, 38(10):1194–1202, April 2020. URL: http://dx.doi.org/10.1038/s41587-020-0505-4, doi:10.1038/s41587-020-0505-4.
missing journal in Lai2024
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