scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires.
- 1School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi'an, Shaanxi 710049, China.
Abstract
Bulk T cell receptor (TCR) sequencing enables scalable immune repertoire profiling but does not measure the transcriptional states required to infer T cell functional programs. Paired single-cell RNA/TCR sequencing offers a single-cell functional prior by jointly observing TCR identity and transcriptomic states. However, transferring this prior to bulk repertoires is challenging: sparse and noisy single-cell profiles can distort the functional reference, and the probabilistic relationship between TCR identity and transcriptome-inferred functional state makes direct cell-level alignment unstable, potentially biasing repertoire-level functional composition estimation. We present scMAC, a single-cell-guided framework for functional context completion that infers TCR functional representations reflecting transcriptome-derived T-cell states. scMAC builds a denoised functional reference by integrating multi-scale expression modeling with gene-semantic priors derived from large language model encodings of gene and protein descriptions, improving gene-level functional coherence. It then learns a prototype-guided TCR-to-function mapping that combines paired cell-level latent alignment with functional prototype regularization, enabling robust repertoire-level functional composition estimation. Across real single-cell and paired RNA/TCR datasets, scMAC outperformed existing single-cell representation methods in fine-grained cell-state representation, gene functional consistency, expression recovery, and disease-relevant pathway analysis, achieving an adjusted Rand index of 0.656 on a colorectal cancer dataset with 83 cell subtypes. For repertoire-level functional composition estimation, scMAC closely matched single-cell-derived functional proportions, with an R2 of 0.979 and a Bray-Curtis distance of 0.075. These results support scMAC as a practical route for transferring single-cell-derived functional knowledge to bulk TCR repertoire analysis beyond clonotype identity and clonal abundance. scMAC is publicly available at https://github.com/Bioinformatics7181/scMAC.
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BibTeX
@article{qian2026scmac,
title = {{scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires.}},
author = {Xinyang Qian and Jiayin Wang},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag586},
url = {https://europepmc.org/article/MED/42932841},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires.
AU - Xinyang Qian
AU - Jiayin Wang
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag586
UR - https://europepmc.org/article/MED/42932841
AB - Bulk T cell receptor (TCR) sequencing enables scalable immune repertoire profiling but does not measure the transcriptional states required to infer T cell functional programs. Paired single-cell RNA/TCR sequencing offers a single-cell functional prior by jointly observing TCR identity and transcriptomic states. However, transferring this prior to bulk repertoires is challenging: sparse and noisy single-cell profiles can distort the functional reference, and the probabilistic relationship between TCR identity and transcriptome-inferred functional state makes direct cell-level alignment unstable, potentially biasing repertoire-level functional composition estimation. We present scMAC, a single-cell-guided framework for functional context completion that infers TCR functional representations reflecting transcriptome-derived T-cell states. scMAC builds a denoised functional reference by integrating multi-scale expression modeling with gene-semantic priors derived from large language model encodings of gene and protein descriptions, improving gene-level functional coherence. It then learns a prototype-guided TCR-to-function mapping that combines paired cell-level latent alignment with functional prototype regularization, enabling robust repertoire-level functional composition estimation. Across real single-cell and paired RNA/TCR datasets, scMAC outperformed existing single-cell representation methods in fine-grained cell-state representation, gene functional consistency, expression recovery, and disease-relevant pathway analysis, achieving an adjusted Rand index of 0.656 on a colorectal cancer dataset with 83 cell subtypes. For repertoire-level functional composition estimation, scMAC closely matched single-cell-derived functional proportions, with an R2 of 0.979 and a Bray-Curtis distance of 0.075. These results support scMAC as a practical route for transferring single-cell-derived functional knowledge to bulk TCR repertoire analysis beyond clonotype identity and clonal abundance. scMAC is publicly available at https://github.com/Bioinformatics7181/scMAC.
SN - 1467-5463
SN - 1477-4054
ER -