Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA) is a research paper published in Nature Biotechnology (2021). On theSindex it has a DataRank of 0. It has been cited 136 times.
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U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health
Grant: R01 AI136514
NIH HHS
Grant: S10 OD028685
National Institutes of Health
Grant: 5R01AI136514-05
Decoding the interactions between T cell receptors and peptide-MHC
National Institutes of Health
Grant: 1S10OD028685-01
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases Research
American Lebanese Syrian Associated Charities
FWCI
8.73
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 3 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 3 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 1 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 1 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 2 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 2 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 1 of GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data
Additional file 1 of GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data
Additional file 2 of GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data
Additional file 2 of GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data
Additional file 5 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 5 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 1 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 1 of Anchor Clustering for million-scale immune repertoire sequencing data
Additional file 3 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 4 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 4 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 3 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 2 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Additional file 2 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles