Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models (LLMs). LLMs are artificial intelligence models trained on vast text corpora to understand and generate human-like text. This package integrates with 'Seurat' objects and provides uncertainty quantification for annotations. Supports various LLM providers including 'OpenAI', 'Anthropic', and 'Google'. The package leverages these models through their respective APIs (Application Programming Interfaces) <https://platform.openai.com/docs>, <https://docs.anthropic.com/>, and <https://ai.google.dev/gemini-api/docs>. For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.
| Version: | 1.3.2 |
| Imports: | dplyr, httr (≥ 1.4.0), jsonlite (≥ 1.7.0), R6 (≥ 2.5.0), digest (≥ 0.6.25), magrittr, utils |
| Suggests: | knitr, rmarkdown, Seurat |
| Published: | 2025-09-02 |
| Author: | Chen Yang [aut, cre, cph] |
| Maintainer: | Chen Yang <cafferychen777 at tamu.edu> |
| BugReports: | https://github.com/cafferychen777/mLLMCelltype/issues |
| License: | MIT + file LICENSE |
| URL: | https://cafferyang.com/mLLMCelltype/ |
| NeedsCompilation: | no |
| Citation: | mLLMCelltype citation info |
| Materials: | README, NEWS |
| CRAN checks: | mLLMCelltype results |
| Reference manual: | mLLMCelltype.html , mLLMCelltype.pdf |
| Vignettes: |
Getting Started with mLLMCelltype (source, R code) Introduction to mLLMCelltype (source, R code) Usage Tutorial (source, R code) Visualization Guide (source, R code) |
| Package source: | mLLMCelltype_1.3.2.tar.gz |
| Windows binaries: | r-devel: mLLMCelltype_1.3.2.zip, r-release: mLLMCelltype_1.3.2.zip, r-oldrel: not available |
| macOS binaries: | r-release (arm64): mLLMCelltype_1.3.2.tgz, r-oldrel (arm64): mLLMCelltype_1.3.2.tgz, r-release (x86_64): mLLMCelltype_1.3.2.tgz, r-oldrel (x86_64): mLLMCelltype_1.3.2.tgz |
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