Knowledge base
==============

Integrating both TCR and GEX information
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**ConGA** :cite:`Schattgen2021` (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** :cite:`Zhang2021` 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** :cite:`Drost2024` 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**: :cite:`Lai2024`

**MIST** :cite:`Zhu2023`

Clustering and similarity search for TCRs
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**GLIPH** :cite:`Glanville2017`

**GLIPH2** :cite:`Huang2020` 

**GIANA** :cite:`Zhang2021b`

**TCRdist1/2** :cite:`Dash2017`

**TCRDist3**: :cite:`MayerBlackwell2022`

**clusTCR** :cite:`Valkiers2021`



Antigen specificity prediction for TCRs
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Methods that consider the structure of the TCR-peptide-MHC complex:

**PISTE** :cite:`Feng2024`

**EPACT** :cite:`Zhang2024`

**TEIM-res** :cite:`Peng2023`

**DeepAIR** :cite:`Zhao2023`


Sequence-level methods:

**TEIM-Seq** :cite:`Peng2023`

**NetTCR-2.0** :cite:`Montemurro2021`

**PanPep**: :cite:`Gao2023`

**DeepTCR** :cite:`Sidhom2021`

**ERGO** :cite:`Springer_2020`

**TCR-BERT** :cite:`pmlr-v240-wu24b`




References
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.. bibliography:: references.bib