Overview

we present a prompt-leaning framework that integrates gene expression data into large language models (LLMs) to generate low-dimensional cell embeddings, called scPT. By generating prompts from gene expression profiles and putting them into transformer layers, scPT enhances the LLM embeddings, effectively fusing expression and text identity information.

Requirements

• Python==3.10

Installation

Start by following this source codes:

conda create -n scPT python=3.10
pip install -r requirements.txt

Data availability

All the data can be found in the supplementary materials of the article.

Tutorial

• You can download the nomic-ai from https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe/tree/main.
• Use train.py to train the model, then you can obtain the data embeddings and model parameters.
• We use result.py to perform the final result analysis for all methods, the results of the spatial data can be found in the spatial folder.