configure_logger

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>.

Chen Yang

mLLMCelltype

Cell Type Annotation Using Large Language Models

configure_logger function

<dl><dt>level</dt>
<dd>Logging level</dd>
<dt>console_output</dt>
<dd>Whether to output to console</dd>
<dt>json_format</dt>
<dd>Whether to use JSON format</dd></dl>

Arguments

Set global logger configuration — configure_logger

<dl>

<dt>level</dt>
<dd>Logging level</dd>


<dt>console_output</dt>
<dd>Whether to output to console</dd>


<dt>json_format</dt>
<dd>Whether to use JSON format</dd>

</dl>

configure_logger: Set global logger configuration

Description

Usage

Arguments