create_annotation_prompt

This file contains all prompt template functions used in mLLMCelltype.
These functions create various prompts for different stages of the cell type annotation process.
Create prompt for cell type annotation

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

create_annotation_prompt function

<dl><dt>input</dt>
<dd>Either the differential gene table returned by Seurat FindAllMarkers() function, or a list of genes</dd>
<dt>tissue_name</dt>
<dd>The name of the tissue</dd>
<dt>top_gene_count</dt>
<dd>Number of top differential genes to use per cluster</dd></dl>

Arguments

Prompt templates for mLLMCelltype — create_annotation_prompt

<dl>

<dt>input</dt>
<dd>Either the differential gene table returned by Seurat FindAllMarkers() function, or a list of genes</dd>


<dt>tissue_name</dt>
<dd>The name of the tissue</dd>


<dt>top_gene_count</dt>
<dd>Number of top differential genes to use per cluster</dd>

</dl>

create_annotation_prompt: Prompt templates for mLLMCelltype

Description

Usage

Value

Arguments